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: "all you need is a good representation a multi level and classifier centric representation for few shot learning"
  Abstract: The main problems of few-shot learning are how to learn a generalized representation and how to construct discriminant classifiers with few-shot samples. We tackle both issues by learning a multi-level representation with a classifier-centric constraint. We first build the multi-level representation by combining three different levels of information: local, global, and higher-level. The resulting representation can characterize new concepts with different aspects and present more universality. To overcome the difficulty of generating classifiers by several shot features, we also propose a classifier-centric loss for learning the representation of each level, which forces samples to be centered on their respective classifier weights in the feature space. Therefore, the multi-level representation learned with classifier-centric constraint not only can enhance the generalization ability, but also can be used to construct the discriminant classifier through a small number of samples. Experiments show that our proposed method, without training or fine-tuning on novel examples, can outperform the current state-of-the-art methods on two low-shot learning datasets. We further show that our approach achieves a significant improvement over baseline method in cross-task validation, and demonstrate its superiority in alleviating the domain shift problem.

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)