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 "selfish robustness and equilibria in multi player bandits"):
  Abstract: Motivated by cognitive radios, stochastic multi-player multi-armed bandits gained a lot of interest recently. In this class of problems, several players simultaneously pull arms and encounter a collision -- with 0 reward -- if some of them pull the same arm at the same time. While the cooperative case where players maximize the collective reward (obediently following some fixed protocol) has been mostly considered, robustness to malicious players is a crucial and challenging concern. Existing approaches consider only the case of adversarial jammers whose objective is to blindly minimize the collective reward. #R##N#We shall consider instead the more natural class of selfish players whose incentives are to maximize their individual rewards, potentially at the expense of the social welfare. We provide the first algorithm robust to selfish players (a.k.a. Nash equilibrium) with a logarithmic regret, when the arm reward is observed. When collisions are also observed, Grim Trigger type of strategies enable some implicit communication-based algorithms and we construct robust algorithms in two different settings: in the homogeneous case (with a regret comparable to the centralized optimal one) and in the heterogeneous case (for an adapted and relevant notion of regret). We also provide impossibility results when only the reward is observed or when arm means vary arbitrarily among players.

Includes members:
  Member "an optimal algorithm in multiplayer multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sic mmab synchronisation involves communication in multiplayer multi armed bandits")
  Member "competing bandits in matching markets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits")
  Member "introduction to multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "multiplayer bandits without observing collision information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "coordination without communication optimal regret in two players multi armed bandits")
  Member "dynamic pricing with finitely many unknown valuations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reserve pricing in repeated second price auctions with strategic bidders")
  Member "sic mmab synchronisation involves communication in multiplayer multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits")
  Member "multi user communication networks a coordinated multi armed bandit approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi player bandits the adversarial case")
  Member "fast rates for bandit optimization with upper confidence frank wolfe":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploration exploitation trade off in reinforcement learning on online markov decision processes with global concave rewards")

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.LG(Machine Learning)