human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "trust estimation and aggregation in peer to peer network using differential gossip algorithm"):
  Abstract: In peer-to-peer network, free riding is a big problem. Reputation management systems are used to overcome free riding. Reputation computation methods generally do not consider the uncertainty caused during reputation computation. This paper proposes a reputation estimation method using BLUE (Best Linear Unbiased estimator) estimator that consider all uncertainties. #R##N#Reputation aggregation in peer to peer networks is generally very time and resource consuming affair. Moreover these methods consider that reputation of a node is same for every node in the network, while that is not true. This paper also proposes a reputation aggregation algorithm that uses a variant of gossip algorithm, differential gossip. Differential gossip is fast and requires sizable amount of resources. Moreover it computes different reputation value of anode for every node in the network. We have implemented Differential Gossip Trust for power law network formed using PA Model. The reputation computed using differential gossip trust shows good immunity to collusion. We have verified the performance of algorithm on power law networks of different sizes ranging from 100 nodes to 50,000 nodes.

Includes members:
  Member "trust estimation in peer to peer network using blue":
    also connected through other hyperedges:
      - 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")
  Member "a game theoretic framework for incentives in p2p systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "computing equilibria in binary networked public goods games")

Question: Please tell me which class the source paper 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)