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: "analysis of thompson sampling for the multi armed bandit problem"
  Abstract: The multi-armed bandit problem is a popular model for studying exploration/exploitation trade-off in sequential decision problems. Many algorithms are now available for this well-studied problem. One of the earliest algorithms, given by W. R. Thompson, dates back to 1933. This algorithm, referred to as Thompson Sampling, is a natural Bayesian algorithm. The basic idea is to choose an arm to play according to its probability of being the best arm. Thompson Sampling algorithm has experimentally been shown to be close to optimal. In addition, it is efficient to implement and exhibits several desirable properties such as small regret for delayed feedback. However, theoretical understanding of this algorithm was quite limited. In this paper, for the first time, we show that Thompson Sampling algorithm achieves logarithmic expected regret for the multi-armed bandit problem. More precisely, for the two-armed bandit problem, the expected regret in time $T$ is $O(\frac{\ln T}{\Delta} + \frac{1}{\Delta^3})$. And, for the $N$-armed bandit problem, the expected regret in time $T$ is $O([(\sum_{i=2}^N \frac{1}{\Delta_i^2})^2] \ln T)$. Our bounds are optimal but for the dependence on $\Delta_i$ and the constant factors in big-Oh.

Connected through hyperedge HE-1 (induced by source paper "effective diversity in population based reinforcement learning"):
  with members:
    - "novelty search for deep reinforcement learning policy network weights by action sequence edit metric distance"
    - "collaborative evolutionary reinforcement learning"
    - "provably robust blackbox optimization for reinforcement learning"
    - "behavior guided reinforcement learning"
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "adapting behaviour for learning progress"
    - "attraction repulsion actor critic for continuous control reinforcement learning"
    - "a generalized framework for population based training"

Connected through hyperedge HE-2 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach"):
  with members:
    - "phase transition analysis for covariance based massive random access with massive mimo"
    - "the roadmap to 6g ai empowered wireless networks"
    - "improved scaling law for activity detection in massive mimo systems"
    - "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"

Connected through hyperedge HE-3 (induced by source paper "regret minimization in stochastic contextual dueling bandits"):
  with members:
    - "combinatorial bandits with relative feedback"
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "multi dueling bandits with dependent arms"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "data driven rank breaking for efficient rank aggregation"
    - "double thompson sampling for dueling bandits"

Connected through hyperedge HE-4 (induced by source paper "sample complexity of incentivized exploration"):
  with members:
    - "introduction to multi armed bandits"
    - "overabundant information and learning traps"
    - "optimal and myopic information acquisition"
    - "descending price optimally coordinates search"
    - "bayesian incentive compatible bandit exploration"
    - "economic recommendation systems"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "bandits with knapsacks"

Connected through hyperedge HE-5 (induced by source paper "feedback graph regret bounds for thompson sampling and ucb"):
  with members:
    - "information directed sampling for stochastic bandits with graph feedback"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "small loss bounds for online learning with partial information"
    - "reward maximization under uncertainty leveraging side observations on networks"
    - "online learning with feedback graphs without the graphs"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "learning to optimize via information directed sampling"

Connected through hyperedge HE-6 (induced by source paper "differentiable bandit exploration"):
  with members:
    - "meta learning of sequential strategies"
    - "empirical bayes regret minimization"
    - "boltzmann exploration done right"
    - "a simple neural attentive meta learner"
    - "algorithms for multi armed bandit problems"
    - "efficient learning in large scale combinatorial semi bandits"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"

Connected through hyperedge HE-7 (induced by source paper "stochastic linear contextual bandits with diverse contexts"):
  with members:
    - "budget constrained edge service provisioning with demand estimation via bandit learning"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "finite time analysis of kernelised contextual bandits"
    - "further optimal regret bounds for thompson sampling"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "efficient optimal learning for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-8 (induced by source paper "rapidly personalizing mobile health treatment policies with limited data"):
  with members:
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "encouraging physical activity in patients with diabetes intervention using a reinforcement learning system"
    - "on kernelized multi armed bandits"
    - "context aware bandits"
    - "a gang of bandits"
    - "sparse gaussian processes for multi task learning"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "portfolio allocation for bayesian optimization"

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)