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: "learning from multiple experts self paced knowledge distillation for long tailed classification"
  Abstract: In real-world scenarios, data tends to exhibit a long-tailed, imbalanced distribution. Developing algorithms to deal with such long-tailed distribution thus becomes indispensable in practical applications. In this paper, we propose a novel self-paced knowledge distillation framework, termed Learning From Multiple Experts (LFME). Our method is inspired by the observation that deep Convolutional Neural Networks (CNNs) trained on less imbalanced subsets of the entire long-tailed distribution often yield better performances than their jointly-trained counterparts. We refer to these models as `Expert Models', and the proposed LFME framework aggregates the knowledge from multiple `Experts' to learn a unified student model. Specifically, the proposed framework involves two levels of self-paced learning schedules: Self-paced Expert Selection and Self-paced Instance Selection, so that the knowledge is adaptively transferred from multiple `Experts' to the `Student'. In order to verify the effectiveness of our proposed framework, we conduct extensive experiments on two long-tailed benchmark classification datasets. The experimental results demonstrate that our method is able to achieve superior performances compared to the state-of-the-art methods. We also show that our method can be easily plugged into state-of-the-art long-tailed classification algorithms for further improvements.

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.CV(Computer Vision and Pattern Recognition)