In Table , we show the performance of our HGAR model on different types of reactions. It is obvious that the long-tail distribution of reaction types in the USPTO-50K dataset, which leads to the critical issue of out-of-distribution(OOD). Additionally, across most reaction types, the inclusion of reaction type information significantly enhances the model's prediction accuracy. This effect is particularly pronounced in the case of "functional group addition" reactions, after incorporating reaction type information, HGAR achieves 100% accuracy for the top-3, 5, and 10 accuracy. It is noteworthy that for "heterocycle formation" reactions, regardless of the inclusion of reaction type information, HGAR's accuracy remains consistently low. This discrepancy may stem from the diverse nature of this reaction category, rendering it challenging to derive patterns governing such reactions.