Generalized Category Discovery (GCD) is a classification task that aims to classify both base and novel classes in unlabeled images, using knowledge from a labeled dataset. In GCD, previous research typically treats scene information as noise and minimizes its influence during model training. However, in this paper, we argue that scene information should not be treated as noise, but rather recognized as a strong prior for inferring novel classes. We attribute the misinterpretation of scene information to a key factor: the Ambiguity Challenge inherent in GCD. Specifically, novel objects in base scenes might be wrongly classified into base categories, while base objects in novel scenes might be mistakenly recognized as novel categories. Once the ambiguity challenge is addressed, scene information can reach its full potential, significantly enhancing the performance of GCD models. To more effectively leverage scene information, we propose the Modeling Object-Scene Associations (MOS) framework, which utilizes a simple MLP-based scene-awareness module to enhance GCD performance. It achieves an exceptional average accuracy of 4\% improvement on the challenging fine-grained datasets compared to state-of-the-art methods, emphasizing its superior performance in GCD tasks.