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: "trust estimation in peer to peer network using blue"
  Abstract: In peer-to-peer networks, free riding is a major problem. Reputation management systems can be used to overcome this problem. Reputation estimation methods generally do not consider the uncertainties in the inputs. We propose a reputation estimation method using BLUE (Best Linear Unbiased estimator) estimator that consider uncertainties in the input variables.

Connected through hyperedge HE-1 (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:
    - "avoiding whitewashing in unstructured peer to peer resource sharing network"

Connected through hyperedge HE-2 (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"
    - "avoiding whitewashing in unstructured peer to peer resource sharing network"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "reputation aggregation in peer to peer network using differential gossip algorithm"):
  with members:
    - "small world file sharing communities"

Connected through hyperedge HE-4 (induced by source paper "trust estimation and aggregation in peer to peer network using differential gossip algorithm"):
  with members:
    - "a game theoretic framework for incentives in p2p systems"

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)