human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "rdpd rich data helps poor data via imitation"
  Abstract: In many situations, we have both rich- and poor- data environments: in a rich-data environment (e.g., intensive care units), we have high-quality multi-modality data. On the other hand, in a poor-data environment (e.g., at home), we often only have access to a single data modality with low quality. How can we learn an accurate and efficient model for the poor-data environment by leveraging multi-modality data from the rich-data environment? In this work, we propose a knowledge distillation model RDPD to enhance a small model trained on poor data with a complex model trained on rich data. In an end-to-end fashion, RDPD trains a student model built on a single modality data (poor data) to imitate the behavior and performance of a teacher model from multimodal data (rich data) via jointly optimizing the combined loss of attention imitation and target imitation. We evaluated RDPD on three real-world datasets. RDPD consistently outperformed all baselines across all three datasets, especially achieving the greatest performance improvement over a standard neural network model trained on the common features (Direct model) by 24.56% on PR-AUC and 12.21% on ROC-AUC, and over the standard knowledge distillation model by 5.91% on PR-AUC and 4.44% on ROC-AUC.

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.LG(Machine Learning)