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: "predicting links and inferring attributes using a social attribute network san"
  Abstract: The eects of social inuence and homophily suggest that both network structure and node attribute information should inform the tasks of link prediction and node attribute inference. Recently, Yin et al. [28, 29] proposed Social-Attribute Network (SAN), an attribute-augmented social network, to integrate network structure and node attributes to perform both link prediction and attribute inference. They focused on generalizing the random walk with restart algorithm to the SAN framework and showed improved performance. In this paper, we extend the SAN framework with several leading supervised and unsupervised link prediction algorithms and demonstrate performance improvement for each algorithm on both link prediction and attribute inference. Moreover, we make the novel observation that attribute inference can help inform link prediction, i.e., link prediction accuracy is further improved by rst inferring missing attributes. We comprehensively evaluate these algorithms and compare them with other existing algorithms using a novel, largescale Google+ dataset, which we make publicly available 1 .

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"
    - "an axiomatic study of objective functions for graph clustering"
    - "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"
    - "shortest path discovery in the multi layered social network"

Connected through hyperedge HE-2 (induced by source paper "exploring hierarchies in online social networks"):
  with members:
    - "inferring social status and rich club effects in enterprise communication networks"
    - "friendship networks and social status"
    - "evolution of social attribute networks measurements modeling and implications using google"

Connected through hyperedge HE-3 (induced by source paper "sybilbelief a semi supervised learning approach for structure based sybil detection"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "social turing tests crowdsourcing sybil detection"
    - "uncovering social network sybils in the wild"

Connected through hyperedge HE-4 (induced by source paper "reciprocal versus parasocial relationships in online social networks"):
  with members:
    - "dynamics of trust reciprocation in heterogenous mmog networks"
    - "directed closure measures for networks with reciprocity"
    - "multi scale dynamics in a massive online social network"
    - "a scalable null model for directed graphs matching all degree distributions in out and reciprocal"
    - "nonparametric link prediction in dynamic networks"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "four degrees of separation"

Connected through hyperedge HE-5 (induced by source paper "a friendship privacy attack on friends and 2 distant neighbors in social networks"):
  with members:
    - "risks of friendships on social networks"
    - "all liaisons are dangerous when all your friends are known to us"

Connected through hyperedge HE-6 (induced by source paper "evolution of social attribute networks measurements modeling and implications using google"):
  with members:
    - "google or google examining the popularity of the new osn"
    - "multi scale dynamics in a massive online social network"
    - "constructing and sampling graphs with a prescribed joint degree distribution"
    - "four degrees of separation"
    - "multiplicative attribute graph model of real world networks"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"
    - "systematic topology analysis and generation using degree correlations"

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.SI(Social and Information Networks)