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: "compact hash code learning with binary deep neural network"
  Abstract: In this work, we firstly propose deep network models and learning algorithms for learning binary hash codes given image representations under both unsupervised and supervised manners. Then, by leveraging the powerful capacity of convolutional neural networks, we propose an end-to-end architecture which jointly learns to extract visual features and produce binary hash codes. Our novel network designs constrain one hidden layer to directly output the binary codes. This addresses a challenging issue in some previous works: optimizing nonsmooth objective functions due to binarization. Additionally, we incorporate independence and balance properties in the direct and strict forms into the learning schemes. Furthermore, we also include similarity preserving property in our objective functions. Our resulting optimizations involving these binary, independence, and balance constraints are difficult to solve. We propose to attack them with alternating optimization and careful relaxation. Experimental results on the benchmark datasets show that our proposed methods compare favorably with the state of the art.

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)