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 with the online bootstrap"
  Abstract: Thompson sampling provides a solution to bandit problems in which new observations are allocated to arms with the posterior probability that an arm is optimal. While sometimes easy to implement and asymptotically optimal, Thompson sampling can be computationally demanding in large scale bandit problems, and its performance is dependent on the model fit to the observed data. We introduce bootstrap Thompson sampling (BTS), a heuristic method for solving bandit problems which modifies Thompson sampling by replacing the posterior distribution used in Thompson sampling by a bootstrap distribution. We first explain BTS and show that the performance of BTS is competitive to Thompson sampling in the well-studied Bernoulli bandit case. Subsequently, we detail why BTS using the online bootstrap is more scalable than regular Thompson sampling, and we show through simulation that BTS is more robust to a misspecified error distribution. BTS is an appealing modification of Thompson sampling, especially when samples from the posterior are otherwise not available or are costly.

Connected through hyperedge HE-1 (induced by source paper "budgeted policy learning for task oriented dialogue systems"):
  with members:
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "switch based active deep dyna q efficient adaptive planning for task completion dialogue policy learning"
    - "integrating planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "a tutorial on thompson sampling"

Connected through hyperedge HE-2 (induced by source paper "old dog learns new tricks randomized ucb for bandit problems"):
  with members:
    - "empirical bayes regret minimization"
    - "perturbed history exploration in stochastic linear bandits"
    - "randomized exploration in generalized linear bandits"
    - "garbage in reward out bootstrapping exploration in multi armed bandits"
    - "new insights into bootstrapping for bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "online stochastic linear optimization under one bit feedback"

Connected through hyperedge HE-3 (induced by source paper "on applications of bootstrap in continuous space reinforcement learning"):
  with members:
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "on optimality of adaptive linear quadratic regulators"
    - "garbage in reward out bootstrapping exploration in multi armed bandits"
    - "new insights into bootstrapping for bandits"
    - "finite time analysis of optimal adaptive policies for linear quadratic systems"
    - "finite time identification in unstable linear systems"

Connected through hyperedge HE-4 (induced by source paper "perturbed history exploration in stochastic linear bandits"):
  with members:
    - "new insights into bootstrapping for bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a tutorial on thompson sampling"
    - "customized nonlinear bandits for online response selection in neural conversation models"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "further optimal regret bounds for thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-5 (induced by source paper "garbage in reward out bootstrapping exploration in multi armed bandits"):
  with members:
    - "perturbed history exploration in stochastic multi armed bandits"
    - "perturbed history exploration in stochastic linear bandits"
    - "new insights into bootstrapping for bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "online stochastic linear optimization under one bit feedback"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-6 (induced by source paper "contextual evaluating contextual multi armed bandit problems in r"):
  with members:
    - "making contextual decisions with low technical debt"
    - "tensorflow a system for large scale machine learning"
    - "algorithms for multi armed bandit problems"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "doubly robust policy evaluation and learning"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-7 (induced by source paper "new insights into bootstrapping for bandits"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "deep exploration via bootstrapped dqn"
    - "online stochastic linear optimization under one bit feedback"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "api design for machine learning software experiences from the scikit learn project"
    - "further optimal regret bounds for thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-8 (induced by source paper "adapting multi armed bandits policies to contextual bandits scenarios"):
  with members:
    - "practical contextual bandits with regression oracles"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "a gang of bandits"
    - "efficient optimal learning for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandits with similarity information"
    - "the offset tree for learning with partial labels"

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)