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 "self organizing networks of information gathering cognitive agents"):
  Abstract: In many scenarios, networks emerge endogenously as cognitive agents establish links in order to exchange information. Network formation has been widely studied in economics, but only on the basis of simplistic models that assume that the value of each additional piece of information is constant. In this paper we present a first model and associated analysis for network formation under the much more realistic assumption that the value of each additional piece of information depends on the type of that piece of information and on the information already possessed: information may be complementary or redundant. We model the formation of a network as a non-cooperative game in which the actions are the formation of links and the benefit of forming a link is the value of the information exchanged minus the cost of forming the link. We characterize the topologies of the networks emerging at a Nash equilibrium (NE) of this game and compare the efficiency of equilibrium networks with the efficiency of centrally designed networks. To quantify the impact of information redundancy and linking cost on social information loss, we provide estimates for the Price of Anarchy (PoA); to quantify the impact on individual information loss we introduce and provide estimates for a measure we call Maximum Information Loss (MIL). Finally, we consider the setting in which agents are not endowed with information, but must produce it. We show that the validity of the well-known "law of the few" depends on how information aggregates; in particular, the "law of the few" fails when information displays complementarities.

Includes members:
  Member "social data offloading in d2d enhanced cellular networks by network formation games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "game theory for multi access edge computing survey use cases and future trends")
  Member "conjoining speeds up information diffusion in overlaying social physical networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "challenges in the decentralised web the mastodon case")
  Member "dynamic network formation with incomplete information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical network formation games")
  Member "a game theoretic analysis of incentives in content production and sharing over peer to peer networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint head selection and airtime allocation for data dissemination in mobile social networks")
  Member "coalition formation games for distributed cooperation among roadside units in vehicular networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sub channel allocation for device to device underlaying full duplex mmwave small cells using coalition formation games")
  Member "coalitional games for distributed collaborative spectrum sensing in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "location privacy in cognitive radio networks a survey")
  Member "minimum cost distributed source coding over a network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic optimization for heterogeneous powered wireless multimedia sensor networks with correlated sources and network coding")
  Member "network information flow with correlated sources":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "capacity bounds for networks with correlated sources and characterisation of distributions by entropies")

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.SI(Social and Information Networks)