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: "an axiomatic study of objective functions for graph clustering"
  Abstract: We investigate axioms that intuitively ought to be satised by graph clustering objective functions. Two tailored for graph clustering objectives are introduced, and the four axioms introduced in previous work on distance based clustering are reformulated and generalized for the graph setting. We show that modularity, a standard objective for graph clustering, does not satisfy all these axioms. This leads us to consider adaptive scale modularity, a variant of modularity, that does satisfy the axioms. Adaptive scale modularity has two parameters, which give greater control over the clustering. Standard graph clustering objectives, such as normalized cut and unnormalized cut, are obtained as special cases of adaptive scale modularity. We furthermore show that adaptive scale modularity does not have a resolution limit. In general, the results of our investigation indicate that the considered axioms cover existing ‘good’ objective functions for graph clustering, and can be used to derive an interesting new family of objectives.

Connected through hyperedge HE-1 (induced by source paper "clustering attributed graphs models measures and methods"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "abacus frequent pattern mining based community discovery in multidimensional networks"
    - "combinatorial analysis of multiple networks"
    - "community detection in networks with node attributes"
    - "efficient community detection in large networks using content and links"
    - "a classification for community discovery methods in complex networks"
    - "predicting links and inferring attributes using a social attribute network san"
    - "shortest path discovery in the multi layered social network"

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)