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 for combinatorial semi bandits"
  Abstract: We study the application of the Thompson sampling (TS) methodology to the stochastic combinatorial multi-armed bandit (CMAB) framework. We analyze the standard TS algorithm for the general CMAB, and obtain the first distribution-dependent regret bound of $O(m\log T / \Delta_{\min}) $ for TS under general CMAB, where $m$ is the number of arms, $T$ is the time horizon, and $\Delta_{\min}$ is the minimum gap between the expected reward of the optimal solution and any non-optimal solution. We also show that one cannot use an approximate oracle in TS algorithm for even MAB problems. Then we expand the analysis to matroid bandit, a special case of CMAB and for which we could remove the independence assumption across arms and achieve a better regret bound. Finally, we use some experiments to show the comparison of regrets of CUCB and CTS algorithms.

Connected through hyperedge HE-1 (induced by source paper "tight lower bounds for combinatorial multi armed bandits"):
  with members:
    - "top k combinatorial bandits with full bandit feedback"
    - "thompson sampling for combinatorial network optimization in unknown environments"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "nearly instance optimal sample complexity bounds for top k arm selection"
    - "combinatorial multi armed bandit with general reward functions"
    - "combinatorial semi bandit with known covariance"
    - "combinatorial cascading bandits"
    - "combinatorial bandits revisited"

Connected through hyperedge HE-2 (induced by source paper "on thompson sampling for smoother than lipschitz bandits"):
  with members:
    - "thompson sampling and approximate inference"
    - "introduction to multi armed 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"
    - "x armed bandits"

Connected through hyperedge HE-3 (induced by source paper "mots minimax optimal thompson sampling"):
  with members:
    - "double explore then commit asymptotic optimality and beyond"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-4 (induced by source paper "an online learning framework for energy efficient navigation of electric vehicles"):
  with members:
    - "efficient online learning for optimizing value of information theory and application to interactive troubleshooting"
    - "online learning under delayed feedback"

Connected through hyperedge HE-5 (induced by source paper "thompson sampling for combinatorial network optimization in unknown environments"):
  with members:
    - "batch size independent regret bounds for the combinatorial multi armed bandit problem"
    - "stochastic online learning with probabilistic graph feedback"
    - "combinatorial bandits with full bandit feedback sample complexity and regret minimization"
    - "bandits with side observations bounded vs logarithmic regret"
    - "analysis of thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "regret bounds for stochastic combinatorial multi armed bandits with linear space complexity"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "information directed sampling for stochastic bandits with graph feedback"

Connected through hyperedge HE-6 (induced by source paper "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"):
  with members:
    - "combinatorial semi bandit in the non stationary environment"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "introduction to online convex optimization"
    - "bandit online learning with unknown delays"
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "sparsity variance and curvature in multi armed bandits"
    - "collaborative filtering bandits"
    - "combinatorial bandits revisited"

Connected through hyperedge HE-7 (induced by source paper "batch size independent regret bounds for the combinatorial multi armed bandit problem"):
  with members:
    - "thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "combinatorial multi armed bandit with general reward functions"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "unifying count based exploration and intrinsic motivation"
    - "combinatorial semi bandit with known covariance"
    - "combinatorial cascading bandits"
    - "combinatorial bandits revisited"

Connected through hyperedge HE-8 (induced by source paper "thompson sampling for a fatigue aware online recommendation system"):
  with members:
    - "dynamic learning of sequential choice bandit problem under marketing fatigue"
    - "thompson sampling for cascading bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "a tutorial on thompson sampling"
    - "thompson sampling for the mnl bandit"
    - "combinatorial multi armed bandit with general reward functions"
    - "cascading bandits learning to rank in the cascade model"

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)