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 "an option and agent selection policy with logarithmic regret for multi agent multi armed bandit problems on random graphs"):
  Abstract: Existing studies of the Multi Agent Multi Armed Bandit (MAMAB) problem, with the exception of a very few, consider the case where the agents observe their neighbors according to a static network graph. They also mostly rely on a running consensus for the estimation of the option rewards. Two of the exceptions consider a problem where agents observe instantaneous rewards and actions of their neighbors through an iid ER graph process based communication strategy. In this paper we propose a UCB based option allocation rule that guarantees logarithmic regret even if the graph depends on the history of choices made by the agents. The paper also proposes a novel communication strategy that significantly outperforms the iid ER graph based communication strategy. In both the ER graph and the dependent graph strategy, the regret is shown to depend on the connectivity of the graph in a particularly interesting way where there exists an optimal connectivity of the graph that is less than the full connectivity of the graph.

Includes members:
  Member "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits")
  Member "on distributed cooperative decision making in multiarmed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a decentralized communication policy for multi agent multi armed bandit problems")

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)