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: "linearconv regenerating redundancy in convolution filters as linear combinations for parameter reduction"
  Abstract: Convolutional Neural Networks (CNNs) show state-of-the-art performance in computer vision tasks. However, convolutional layers of CNNs are known to learn redundant features, still not being efficient in memory requirement. In this work, we explore the redundancy of learned features in the form of correlation between convolutional filters, and propose a novel layer to reproduce it efficiently. The proposed "LinearConv" layer generates a portion of convolutional filters as a learnable linear combination of the rest of the filters and introduces a correlation-based regularization to achieve flexibility and control over the correlation between filters, and the number of parameters, in turn. This is developed as a plug-in layer to conveniently replace a conventional convolutional layer without any modification required in the network architecture. Our experiments verify that the LinearConv-based models are able to achieve a performance on-par with counterparts with up to 50% of parameter reduction, and having same computational requirement in run time.

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)