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 "discrete preference games social influence through coordination and beyond"):
  Abstract: We study discrete preference gamesthat have been used to model issues such as the formation of opinions or the adoption of innovations in the context of a social network. In these games, the payoff of each agent depends on the agreement of her strategy to her internal belief and on its coordination with the strategies of her neighbors in the social network. Recent papers have obtained bounds on the price of anarchy and stability of these games. Our starting point is the observation that such bounds can be very sensitive on the way the quality of equilibria is quantified. This motivates us to foc us on properties of the games that do not depend on these details. In particular, we consider the question of whether the public opinion can be different than the public “belief”. We show that strategi c behavior typically leads to changes of the public opinion compared to the public belief. We completely characterize the social networks (graphs) where such changes can happen and furthermore study the complexity of such transitions. We show that deciding whether a minority belief can become the majority opinion is NP-hard even when the initial number of supporters of this belief is very c lose to 1/4 of the social network size. Next, motivated by the limited expressive power of discrete preference games, we define and study the novel class of generalized discrete preference games . These games have additional characteristics and can model social relations to allies or compet itors, including complex relations between more than two agents, introduce different levels of strengt h for each relation, and personalize the dependence of each agent to its neighborhood. We show that these games admit generalized ordinal potential functions. More importantly, we show that every game with two strategies per agent that admits a generalized ordinal potential function is structu rally equivalent to a generalized discrete preference game. This implies that the games in the novel class capture the full generality of twostrategy games in which the existence of (pure) equilibria is guaranteed by topological arguments.

Includes members:
  Member "reaching consensus via non bayesian asynchronous learning in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asynchronous majority dynamics in preferential attachment trees")
  Member "short sequences of improvement moves lead to approximate equilibria in constraint satisfaction games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "congestion games with complementarities")
  Member "decentralized dynamics for finite opinion games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "local aggregation in preference games")
  Member "on discrete preferences and coordination":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local aggregation in preference games")
  Member "how bad is forming your own opinion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial perturbations of opinion dynamics in networks")
  Member "efficient computation of approximate pure nash equilibria in congestion games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "existence and complexity of approximate equilibria in weighted congestion 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.GT(Computer Science and Game Theory)