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: "sybil resilient conductance based community expansion"
  Abstract: Preventing fake or duplicate e-identities (aka sybils) from joining an e-community may be crucial to its survival, especially if it utilizes a consensus protocol among its members or employs democratic governance, where sybils can undermine consensus, tilt decisions, or even take over. Here, we explore the use of a trust graph of identities, with trust edges representing trust among identity owners, to allow a community to grow without increasing its sybil penetration. Since identities are admitted to the e-community based on their trust by existing e-community members, corrupt identities, which may trust sybils, also pose a threat to the e-community. Sybils and their corrupt perpetrators are together referred to as Byzantines, and our overarching aim is to limit their penetration into an e-community. Our key tool in achieving this is graph conductance, and our key assumption is that honest people are averse to corrupt ones and tend to distrust them. Of particular interest is keeping the fraction of Byzantines below one third, as it would allow the use of Byzantine Agreement [13] for consensus as well as for sybil-resilient social choice [16,20] and sybil-resilient parameter update [19]. We consider sequences of expanding trust graphs and show that, under our key assumption and additional requirements from the expansion process, including keeping the conductance of the community trust graph sufficiently high, a community may expand safely.

Connected through hyperedge HE-1 (induced by source paper "sybil resilient reality aware social choice"):
  with members:
    - "sybil resilient conductance based community growth"
    - "incorporating reality into social choice"
    - "robustness among multiwinner voting rules"
    - "sybil proof mechanisms in query incentive 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)