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: "on the prior sensitivity of thompson sampling"
  Abstract: The empirically successful Thompson Sampling algorithm for stochastic bandits has drawn much interest in understanding its theoretical properties. One important benefit of the algorithm is that it allows domain knowledge to be conveniently encoded as a prior distribution to balance exploration and exploitation more effectively. While it is generally believed that the algorithm's regret is low (high) when the prior is good (bad), little is known about the exact dependence. In this paper, we fully characterize the algorithm's worst-case dependence of regret on the choice of prior, focusing on a special yet representative case. These results also provide insights into the general sensitivity of the algorithm to the choice of priors. In particular, with $p$ being the prior probability mass of the true reward-generating model, we prove $O(\sqrt{T/p})$ and $O(\sqrt{(1-p)T})$ regret upper bounds for the bad- and good-prior cases, respectively, as well as \emph{matching} lower bounds. Our proofs rely on the discovery of a fundamental property of Thompson Sampling and make heavy use of martingale theory, both of which appear novel in the literature, to the best of our knowledge.

Connected through hyperedge HE-1 (induced by source paper "meta dynamic pricing learning across experiments"):
  with members:
    - "regret bounds for meta bayesian optimization with an unknown gaussian process prior"
    - "learning to route efficiently with end to end feedback the value of networked structure"
    - "meta learning for contextual bandit exploration"
    - "probabilistic model agnostic meta learning"
    - "a tutorial on thompson sampling"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "bandits with concave rewards and convex knapsacks"
    - "learning to optimize via posterior sampling"

Connected through hyperedge HE-2 (induced by source paper "bandit learning through biased maximum likelihood estimation"):
  with members:
    - "learning to optimize via information directed sampling"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "optimism in reinforcement learning and kullback leibler divergence"

Connected through hyperedge HE-3 (induced by source paper "cuttlefish a lightweight primitive for adaptive query processing"):
  with members:
    - "distributed clustering of linear bandits in peer to peer networks"
    - "on context dependent clustering of bandits"
    - "comparative evaluation of big data systems on scientific image analytics workloads"
    - "caffe con troll shallow ideas to speed up deep learning"
    - "mllib machine learning in apache spark"
    - "show attend and tell neural image caption generation with visual attention"
    - "rethinking the inception architecture for computer vision"
    - "online clustering of bandits"

Connected through hyperedge HE-4 (induced by source paper "bayesian reinforcement learning a survey"):
  with members:
    - "benchmarking for bayesian reinforcement learning"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "a greedy approximation of bayesian reinforcement learning with probably optimistic transition model"
    - "model based bayesian exploration"
    - "further optimal regret bounds for thompson sampling"
    - "model based bayesian reinforcement learning in large structured domains"
    - "near optimal brl using optimistic local transitions"

Connected through hyperedge HE-5 (induced by source paper "the pareto regret frontier for bandits"):
  with members:
    - "optimally confident ucb improved regret for finite armed bandits"
    - "further optimal regret bounds for thompson sampling"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "prediction strategies without loss"
    - "adaptive online prediction by following the perturbed leader"

Connected through hyperedge HE-6 (induced by source paper "regret analysis of the finite horizon gittins index strategy for multi armed bandits"):
  with members:
    - "the pareto regret frontier for bandits"
    - "optimally confident ucb improved regret for finite armed bandits"
    - "further optimal regret bounds for thompson sampling"

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)