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: "cross modal subspace learning with kernel correlation maximization and discriminative structure preserving"
  Abstract: The measure between heterogeneous data is still an open problem. Many research works have been developed to learn a common subspace where the similarity between different modalities can be calculated. However, most of existing works focus on learning low dimensional subspace and ignore the loss of discriminative information in process of reducing dimension. Thus, these approaches cannot get the results they expected. On basis of the Hilbert space theory in which different Hilbert spaces but with same dimension are isomorphic, we propose a novel framework where the multiple use of label information can facilitate more discriminative subspace representation to learn isomorphic Hilbert space for each modal. Our model not only considers the inter-modality correlation by maximizing the kernel correlation, but also preserves the structure information within each modal according to constructed graph model. Extensive experiments are performed to evaluate the proposed framework, termed Cross-modal subspace learning with Kernel correlation maximization and Discriminative structure preserving (CKD), on the three public datasets. Experimental results demonstrated the competitive performance of the proposed CKD compared with the classic subspace learning methods.

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)