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 "a game theoretic model for the formation of navigable small world networks the balance between distance and reciprocity"):
  Abstract: Kleinberg proposed a family of small-world networks to explain the navigability of large-scale real-world social networks. However, the underlying mechanism that drives real networks to be navigable is not yet well understood. In this paper, we present a game theoretic model for the formation of navigable small world networks. We model the network formation as a Distance-Reciprocity Balanced (DRB) game in which people seek for both high reciprocity and long-distance relationships. We show that the game has only two Nash equilibria: One is the navigable small-world network, and the other is the random network in which each node connects with each other node with equal probability. We further show that the navigable small world is very stable --- (a) no collusion of any size would benefit from deviating from it; and (b) after an arbitrary deviations of a large random set of nodes, the network would return to the navigable small world as soon as every node takes one best-response step. In contrast, for the random network, a small group collusion or random perturbations is guaranteed to move the network to the navigable network as soon as every node takes one best-response step. Moreover, we show that navigable small world has much better social welfare than the random network, and provide the price-of-anarchy and price-of-stability results of the game. Our empirical evaluation demonstrates that the system always converges to the navigable network even when limited or no information about other players' strategies is available, and the DRB game simulated on the real-world network leads to navigability characteristic that is very close to that of the real network. Our theoretical and empirical analyses provide important new insight on the connection between distance, reciprocity and navigability in social networks.

Includes members:
  Member "evolution of social attribute networks measurements modeling and implications using google":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges")
  Member "uncovering social network sybils in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")
  Member "distance is not dead social interaction and geographical distance in the internet era":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "competition between homophily and information entropy maximization in social networks")
  Member "networks become navigable as nodes move and forget":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "greedy routing and the algorithmic small world phenomenom")
  Member "the evolution of navigable small world networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "evolution of the media web")

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)