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: "extended ucb policy for multi armed bandit with light tailed reward distributions"
  Abstract: We consider the multi-armed bandit problems in which a player aims to accrue reward#N#         by sequentially playing a given set of arms with unknown reward statistics. In the classic#N#         work, policies were proposed to achieve the optimal logarithmic regret order for some#N#         special classes of light-tailed reward distributions, e.g., Auer et al.'s UCB1 index policy#N#         for reward distributions with finite support. In this paper, we extend Auer et al.'s UCB1#N#         index policy to achieve the optimal logarithmic regret order for all light-tailed (or#N#         equivalently, locally sub-Gaussian) reward distributions defined by the (local) existence#N#         of the moment-generating function.

Connected through hyperedge HE-1 (induced by source paper "risk averse multi armed bandit problems under mean variance measure"):
  with members:
    - "exploration vs exploitation vs safety risk averse multi armed bandits"
    - "generalized risk aversion in stochastic multi armed bandits"
    - "risk aversion in multi armed bandits"
    - "linear bandits in high dimension and recommendation systems"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-2 (induced by source paper "on distributed cooperative decision making in multiarmed bandits"):
  with members:
    - "modeling human decision making in generalized gaussian multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"

Connected through hyperedge HE-3 (induced by source paper "satisficing in multi armed bandit problems"):
  with members:
    - "robots that can adapt like animals"

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)