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: "the role of strong and weak ties in facebook a community structure perspective"
  Abstract: Social dynamics explaining communication among individuals and social relationships represent a major eld of investigation in the current scenario of online social network research, whose importance and impact in everyday life is growing at unprecedented rate. This work reports our quantitative assessment of so-called strength of weak ties theory, foreseen by Granovetter’s seminal studies, applied to well-known Facebook social graph. We characterize the features of weak/strong ties considering the community structure of this network, studying a sample available in literature containing millions of users and their social relationships: after discovering the communities of aggregation among users, we classify ties according to the original Granovetter’s, and study the statistical features of such ties, discovering clearly emerging organization patterns.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "social structure of facebook networks"
    - "comparative evaluation of community detection algorithms a topological approach"

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)