docs: unify "belong to" for class membership across docs and docstring

The markdown said "each image should match exactly one" and the docstring
said "each image should fit exactly one" for the same idea. A reader
crossing between the docs site and the SDK reference would see drift.
Standardize on "belong to" — the precise class-membership term in ML
literature.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: bwada

docs/docs/answer-modes/2-multi-choice-detectors.md

@@ -17,7 +17,7 @@ detector = gl_exp.create_multiclass_detector(
 ```
 
 :::caution Use non-overlapping classes
-Multi-class detectors are designed for **mutually exclusive** classes — each image should match exactly one. They always return a single label and are **not** multi-label classifiers.
+Multi-class detectors are designed for **mutually exclusive** classes — each image should belong to exactly one. They always return a single label and are **not** multi-label classifiers.
 
 A quick test: if your question is *"Which one of these is it?"*, a multi-class detector fits. If it's *"Which of these are present?"* — meaning more than one could be true at once — create a separate detector for each question you want to answer instead.
 :::

src/groundlight/client.py

@@ -1680,7 +1680,7 @@ def create_multiclass_detector(  # noqa: PLR0913 # pylint: disable=too-many-argu
 
         A multiclass detector returns exactly one label per image, chosen from
         ``class_names``. It is designed for **mutually exclusive
-        (non-overlapping)** classes — each image should fit exactly one.
+        (non-overlapping)** classes — each image should belong to exactly one.
         Multi-class detectors are **not** multi-label classifiers; if your
         classes can co-occur in the same image (e.g. an image can contain both
         a person and a dog), create a separate detector for each question you