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: "x armed bandits"
  Abstract: We consider a generalization of stochastic bandits where the set of arms, $\cX$, is allowed to be a generic measurable space and the mean-payoff function is "locally Lipschitz" with respect to a dissimilarity function that is known to the decision maker. Under this condition we construct an arm selection policy, called HOO (hierarchical optimistic optimization), with improved regret bounds compared to previous results for a large class of problems. In particular, our results imply that if $\cX$ is the unit hypercube in a Euclidean space and the mean-payoff function has a finite number of global maxima around which the behavior of the function is locally continuous with a known smoothness degree, then the expected regret of HOO is bounded up to a logarithmic factor by $\sqrt{n}$, i.e., the rate of growth of the regret is independent of the dimension of the space. We also prove the minimax optimality of our algorithm when the dissimilarity is a metric. Our basic strategy has quadratic computational complexity as a function of the number of time steps and does not rely on the doubling trick. We also introduce a modified strategy, which relies on the doubling trick but runs in linearithmic time. Both results are improvements with respect to previous approaches.

Connected through hyperedge HE-1 (induced by source paper "on thompson sampling for smoother than lipschitz bandits"):
  with members:
    - "thompson sampling and approximate inference"
    - "introduction to multi armed bandits"
    - "thompson sampling for combinatorial semi bandits"
    - "a tutorial on thompson sampling"
    - "learning to optimize via posterior sampling"
    - "bandits and experts in metric spaces"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-2 (induced by source paper "nonparametric pricing analytics with customer covariates"):
  with members:
    - "a primal dual learning algorithm for personalized dynamic pricing with an inventory constraint"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "algorithms for multi armed bandit problems"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "contextual bandits with similarity information"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-3 (induced by source paper "procrastinating with confidence near optimal anytime adaptive algorithm configuration"):
  with members:
    - "learning to branch"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "a pac approach to application specific algorithm selection"
    - "bandits with knapsacks"

Connected through hyperedge HE-4 (induced by source paper "stochastic lipschitz q learning"):
  with members:
    - "is q learning provably efficient"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "deep visual foresight for planning robot motion"
    - "asynchronous methods for deep reinforcement learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "trust region policy optimization"
    - "learning continuous control policies by stochastic value gradients"

Connected through hyperedge HE-5 (induced by source paper "exploiting relevance for online decision making in high dimensions"):
  with members:
    - "multi objective contextual multi armed bandit with a dominant objective"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "releaf an algorithm for learning and exploiting relevance"
    - "adaptive ensemble learning with confidence bounds"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "feature selection via regularized trees"
    - "efficient optimal learning for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "lipschitz bandit optimization with improved efficiency"):
  with members:
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "exponential regret bounds for gaussian process bandits with deterministic observations"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandits with similarity information"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-7 (induced by source paper "derivative free order robust optimisation"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "introduction to online convex optimization"
    - "a simple parameter free and adaptive approach to optimization under a minimal local smoothness assumption"
    - "overcoming catastrophic forgetting in neural networks"
    - "bandit algorithms for tree search"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-8 (induced by source paper "automated machine learning with monte carlo tree search"):
  with members:
    - "autostacker a compositional evolutionary learning system"
    - "benchmarking automatic machine learning frameworks"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "group equivariant convolutional networks"
    - "openml networked science in machine learning"
    - "bandit algorithms for tree search"

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)