human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "distributed multi armed bandits regret vs communication"
  Abstract: In this paper, we generalize both the model and the results of the classical multi-armed bandit problem to a distributed setting, where a common arm set is shared by multiple players in a non-conflicting way. Moreover, the players receive the rewards independently and are allowed to communicate with each other after some prescribed rounds which are given as the elements of a \textsl{communication set}. In particular, we study how communication can help to reduce the \textsl{regret}. We propose a novel concept to measure the frequency of communication --- the \textsl{density} of the communication set, which is used to establish a non-trivial lower bound for the expected regret of \textsl{any} \textsl{consistent} policy. Furthermore, we develop a distributed policy \textsc{Dklucb} that can achieve the lower bound in the case of Bernoulli rewards. Compared to existing policies such as KL-UCB for classical multi-armed bandit problems, a crucial ingredient of \textsc{Dklucb} is to use a "fake" pull count. The analysis of the algorithm also becomes much more complex, requiring new tools and techniques. Finally, we discuss a possible extension of \textsc{Dklucb} for more general distributions.

Connected through hyperedge HE-1 (induced by source paper "collaborative learning of stochastic bandits over a social network"):
  with members:
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "delay and cooperation in nonstochastic bandits"
    - "stochastic bandits on a social network collaborative learning with local information sharing"
    - "regret vs communication distributed stochastic multi armed bandits and beyond"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "online learning with feedback graphs beyond bandits"
    - "the domination number of on line social networks and random geometric graphs"
    - "multiarmed bandits with limited expert advice"

Question: Please tell me which class the center node 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)