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 "decentralized cooperative stochastic multi armed bandits"):
  Abstract: We study a decentralized cooperative stochastic multi-armed bandit problem with $K$ arms on a network of $N$ agents. In our model, the reward distribution of each arm is agent-independent. Each agent chooses iteratively one arm to play and then communicates to her neighbors. The aim is to minimize the total network regret. We design a fully decentralized algorithm that uses a running consensus procedure to compute, with some delay, accurate estimations of the average of rewards obtained by all the agents for each arm, and then uses an upper confidence bound algorithm that accounts for the delay and error of the estimations. We analyze the algorithm and up to a constant our regret bounds are better for all networks than other algorithms designed to solve the same problem. For some graphs, our regret bounds are significantly better.

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 "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 "on distributed cooperative decision making in multiarmed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a decentralized communication policy for multi agent multi armed bandit problems")
  Member "distributed exploration in multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits")
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inverse graph learning over optimization networks")

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)