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: "cluster wise unsupervised hashing for cross modal similarity search"
  Abstract: In this paper, we present a new cluster-wise unsupervised hashing (CUH) approach to learn compact binary codes for cross-modal similarity retrieval. We develop a discrete optimization method to jointly learn binary codes and the corresponding hash functions for each modality which can improve the performance, unlike existing cross-modal hashing methods that often drop the binary constraints to obtain the binary codes. Moreover, considering the semantic consistency between observed modalities, our CUH generates one unified hash code for all observed modalities of any instance. Specifically, we construct a co-training framework for learning to hash, in which we simultaneously realize the multi-view clustering and the learning of hash. Firstly, our CUH utilize the re-weighted discriminatively embedded K-means for multi-view clustering to learn the corresponding dimension reduced data and the cluster centroid points in the low-dimensional common subspaces, which are used as the approximation to the corresponding hash codes of original data and the cluster-wise code-prototypes respectively. Secondly, in the process for learning of hash, these cluster-wise code-prototypes can guide the learning of the codes to further improve the performance of the binary codes. The reasonableness and effectiveness of CUH is well demonstrated by comprehensive experiments on diverse benchmark datasets.

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)