SYSUpporter Team at BEA 2025 Shared Task: Class Compensation and Assignment Optimization for LLM-generated Tutor Identification

In this paper, we propose a novel framework for the tutor identification track of the BEA 2025 shared task (Track 5).Our framework integrates data-algorithm co-design, dynamic class compensation, and structured prediction optimization.Specifically, our approach employs noise augmentation, a fine-tuned DeBERTa-v3small model with inverse-frequency weighted loss, and Hungarian algorithm-based label assignment to address key challenges, such as severe class imbalance and variable-length dialogue complexity.Our method achieved 0.969 Macro-F1 score on the official test set, securing second place in this competition.Ablation studies revealed significant improvements: a 9.4% gain in robustness from data augmentation, a 5.3% boost in minority-class recall thanks to the weighted loss, and a 2.1% increase in Macro-F1 score through Hungarian optimization.This work advances the field of educational AI by providing a solution for tutor identification, with implications for quality control in LLM-assisted learning environments.