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: "avoiding whitewashing in unstructured peer to peer resource sharing network"
  Abstract: In peer-to-peer file sharing network, it is hard to distinguish between a legitimate newcomer and a whitewasher. This makes whitewashing a big problem in peer-to-peer networks. Although the problem of whitewashing can be solved using permanent identities, it may take away the right of anonymity for users. In this paper, we a have proposed a novel algorithm to avoid this problem when network uses free temporary identities. In this algorithm, the initial reputation is adjusted according to the level of whitewashing in the network.

Connected through hyperedge HE-1 (induced by source paper "evolutionary stability of reputation management system in peer to peer networks"):
  with members:
    - "reputation based probabilistic resource allocation for avoiding free riding and formation of common interest groups in unstructured p2p networks"

Connected through hyperedge HE-2 (induced by source paper "reputation based probabilistic resource allocation for avoiding free riding and formation of common interest groups in unstructured p2p networks"):
  with members:
    - "trust estimation in peer to peer network using blue"

Connected through hyperedge HE-3 (induced by source paper "resource allocation in peer to peer networks a control theoretical perspective"):
  with members:
    - "reputation based probabilistic resource allocation for avoiding free riding and formation of common interest groups in unstructured p2p networks"
    - "trust estimation in peer to peer network using blue"
    - "trust estimation and aggregation in peer to peer network using differential gossip algorithm"
    - "mapping the gnutella network properties of large scale peer to peer systems and implications for system design"

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.NI(Networking and Internet Architecture)