HotBugs.jar is a novel benchmark targeting time-critical (a.k.a. hot) fixes. We propose an approach to analyze the taxonomy of the bugs in HotBugs.jar, by extending PatchCat into HotCat, integrating hotfix metadata with multi-objective optimization. Using NSGA-II, we evolve bitmask-based feature subsets that balance accuracy, Normalized Mutual Information (NMI), and runtime. On 88 records across 17 categories, HotCat achieved 0.59 accuracy and 0.58 NMI at 129s, with maximum accuracy of 0.63 at 132s, demonstrating accuracy improvements without additional resource use, thus supporting sustainability. Future work will expand and augment the dataset, refine optimization objectives, and improve semantic categorization, robustness, and cluster balance.