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: "towards realistic artificial benchmark for community detection algorithms evaluation"
  Abstract: Many algorithms have been proposed for revealing the community structure in complex networks. Tests under a wide range of realistic conditions must be performed in order to select the most appropriate for a particular application. Artificially generated networks are often used for this purpose. The most realistic generative method to date has been proposed by Lancichinetti, Fortunato and Radicchi LFR. However, it does not produce networks with some typical features of real-world networks. To overcome this drawback, we investigate two alternative modifications of this algorithm. Experimental results show that in both cases, centralisation and degree correlation values of generated networks are closer to those encountered in real-world networks. The three benchmarks have been used on a wide set of prominent community detection algorithms in order to reveal the limits and the robustness of the algorithms. Results show that the detection of meaningful communities gets harder with more realistic networks, and particularly when the proportion of inter-community links increases.

Connected through hyperedge HE-1 (induced by source paper "ensemble clustering for graphs comparisons and applications"):
  with members:
    - "ensemble clustering for graphs"
    - "comparing graph clusterings set partition measures vs graph aware measures"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "comparative evaluation of community detection algorithms a topological approach"

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"
    - "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"
    - "a classification for community discovery methods in complex networks"

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"
    - "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-4 (induced by source paper "a framework for the construction of generative models for mesoscale structure in multilayer networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "random graph models for dynamic networks"
    - "the many facets of community detection in complex networks"
    - "the geometric block model"
    - "block models and personalized pagerank"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "multiresolution consensus clustering in networks"
    - "the ground truth about metadata and community detection in networks"

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)