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: "on accuracy of community structure discovery algorithms"
  Abstract: Community structure discovery in complex networks is a quite challenging problem spanning many applications in various disciplines such as biology, social network and physics. Emerging from various approaches numerous algorithms have been proposed to tackle this problem. Nevertheless little attention has been devoted to compare their efficiency on realistic simulated data. To better understand their relative performances, we evaluate systematically eleven algorithms covering the main approaches. The Normalized Mutual Information (NMI) measure is used to assess the quality of the discovered community structure from controlled artificial networks with realistic topological properties. Results show that along with the network size, the average proportion of intra-community to inter-community links is the most influential parameter on performances. Overall, "Infomap" is the leading algorithm, followed by "Walktrap", "SpinGlass" and "Louvain" which also achieve good consistency.

Connected through hyperedge HE-1 (induced by source paper "a general framework for complex network based image segmentation"):
  with members:
    - "segmentation of large images based on super pixels and community detection in graphs"
    - "action2activity recognizing complex activities from sensor data"
    - "qualitative comparison of community detection algorithms"

Connected through hyperedge HE-2 (induced by source paper "centrality in modular networks"):
  with members:
    - "social centrality using network hierarchy and community structure"
    - "immunization of networks with non overlapping community structure"
    - "centrality measures for networks with community structure"
    - "community based immunization strategies for epidemic control"
    - "towards realistic artificial benchmark for community detection algorithms evaluation"
    - "the role of strong and weak ties in facebook a community structure perspective"
    - "social structure of facebook networks"
    - "comparative evaluation of community detection algorithms a topological approach"

Connected through hyperedge HE-3 (induced by source paper "immunization of networks with non overlapping community structure"):
  with members:
    - "centrality in modular networks"
    - "centrality measures for networks with community structure"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "community based immunization strategies for epidemic control"
    - "an empirical study of the relation between community structure and transitivity"
    - "towards realistic artificial benchmark for community detection algorithms evaluation"
    - "the role of strong and weak ties in facebook a community structure perspective"
    - "social structure of facebook networks"

Connected through hyperedge HE-4 (induced by source paper "non overlapping community detection"):
  with members:
    - "surprise maximization reveals the community structure of complex networks"
    - "comparative evaluation of community detection algorithms a topological approach"
    - "defining and evaluating network communities based on ground truth"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-5 (induced by source paper "towards realistic artificial benchmark for community detection algorithms evaluation"):
  with members:
    - "an empirical study of the relation between community structure and transitivity"
    - "qualitative comparison of community detection algorithms"

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)