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: "leveraging deep neural networks to capture psychological representations"
  Abstract: Artificial neural networks have seen a recent surge in popularity for their ability to solve complex problems as well as or better than humans. In computer vision, deep convolutional neural networks have become the standard for object classification and image understanding due to their ability to learn efficient representations of high-dimensional data. However, the relationship between these representations and human psychological representations has remained unclear. Here we evaluate the quantitative and qualitative nature of this correspondence. We find that state-of-the-art object classification networks provide a reasonable first approximation to human similarity judgments, but fail to capture some of the structure of psychological representations. We show that a simple transformation that corrects these discrepancies can be obtained through convex optimization. Such representations provide a tool that can be used to study human performance on complex tasks with naturalistic stimuli, such as predicting the difficulty of learning novel categories. Our results extend the scope of psychological experiments and computational modeling of cognition by enabling tractable use of large natural stimulus sets.

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)