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 "multi player bandits the adversarial case"):
  Abstract: We consider a setting where multiple players sequentially choose among a common set of actions (arms). Motivated by a cognitive radio networks application, we assume that players incur a loss upon colliding, and that communication between players is not possible. Existing approaches assume that the system is stationary. Yet this assumption is often violated in practice, e.g., due to signal strength fluctuations. In this work, we design the first Multi-player Bandit algorithm that provably works in arbitrarily changing environments, where the losses of the arms may even be chosen by an adversary. This resolves an open problem posed by Rosenski, Shamir, and Szlak (2016).

Includes members:
  Member "multi user communication networks a coordinated multi armed bandit approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "selfish robustness and equilibria in multi player bandits")
  Member "delay and cooperation in nonstochastic bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits")
  Member "multi user lax communications a multi armed bandit approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits")
  Member "multi player bandits a musical chairs approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits")
  Member "concurrent bandits and cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits")
  Member "online bandit learning against an adaptive adversary from regret to policy regret":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "logarithmic regret for adversarial online control")
  Member "distributed algorithms for learning and cognitive medium access with logarithmic regret":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits")

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)