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: "thompson sampling is asymptotically optimal in general environments"
  Abstract: We discuss a variant of Thompson sampling for nonparametric reinforcement learning in a countable classes of general stochastic environments. These environments can be non-Markov, non-ergodic, and partially observable. We show that Thompson sampling learns the environment class in the sense that (1) asymptotically its value converges to the optimal value in mean and (2) given a recoverability assumption regret is sublinear.

Connected through hyperedge HE-1 (induced by source paper "optimization of a ssp s header bidding strategy using thompson sampling"):
  with members:
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "online learning in repeated auctions"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-2 (induced by source paper "universal reinforcement learning algorithms survey and experiments"):
  with members:
    - "count based exploration in feature space for reinforcement learning"
    - "generalised discount functions applied to a monte carlo aimu implementation"
    - "nonparametric general reinforcement learning"
    - "vime variational information maximizing exploration"
    - "unifying count based exploration and intrinsic motivation"
    - "bad universal priors and notions of optimality"
    - "on the computability of solomonoff induction and knowledge seeking"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "value directed exploration in multi armed bandits with structured priors"):
  with members:
    - "regret analysis of the finite horizon gittins index strategy for multi armed bandits"
    - "learning to optimize via information directed sampling"
    - "learning to optimize via posterior sampling"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-4 (induced by source paper "unifying count based exploration and intrinsic motivation"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "vime variational information maximizing exploration"
    - "pixel recurrent neural networks"
    - "state of the art control of atari games using shallow reinforcement learning"
    - "prioritized experience replay"
    - "increasing the action gap new operators for reinforcement learning"
    - "action conditional video prediction using deep networks in atari games"
    - "incentivizing exploration in reinforcement learning with deep predictive models"

Connected through hyperedge HE-5 (induced by source paper "a formal solution to the grain of truth problem"):
  with members:
    - "nonparametric general reinforcement learning"
    - "bad universal priors and notions of optimality"
    - "on the computability of aixi"
    - "on the computability of solomonoff induction and knowledge seeking"
    - "reflective oracles a foundation for classical game theory"
    - "open problems in universal induction intelligence"

Connected through hyperedge HE-6 (induced by source paper "self modification of policy and utility function in rational agents"):
  with members:
    - "avoiding wireheading with value reinforcement learning"
    - "bad universal priors and notions of optimality"
    - "extreme state aggregation beyond mdps"
    - "model based utility functions"
    - "universal intelligence a definition of machine intelligence"

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)