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: "first order regret analysis of thompson sampling"
  Abstract: We address online combinatorial optimization when the player has a prior over the adversary's sequence of losses. In this framework, Russo and Van Roy proposed an information-theoretic analysis of Thompson Sampling based on the {\em information ratio}, resulting in optimal worst-case regret bounds. In this paper we introduce three novel ideas to this line of work. First we propose a new quantity, the scale-sensitive information ratio, which allows us to obtain more refined first-order regret bounds (i.e., bounds of the form $\sqrt{L^*}$ where $L^*$ is the loss of the best combinatorial action). Second we replace the entropy over combinatorial actions by a coordinate entropy, which allows us to obtain the first optimal worst-case bound for Thompson Sampling in the combinatorial setting. Finally, we introduce a novel link between Bayesian agents and frequentist confidence intervals. Combining these ideas we show that the classical multi-armed bandit first-order regret bound $\tilde{O}(\sqrt{d L^*})$ still holds true in the more challenging and more general semi-bandit scenario. This latter result improves the previous state of the art bound $\tilde{O}(\sqrt{(d+m^3)L^*})$ by Lykouris, Sridharan and Tardos.

Connected through hyperedge HE-1 (induced by source paper "a closer look at small loss bounds for bandits with graph feedback"):
  with members:
    - "equipping experts bandits with long term memory"
    - "bandits with feedback graphs and switching costs"
    - "improved path length regret bounds for bandits"
    - "online learning with feedback graphs and switching costs"
    - "make the minority great again first order regret bound for contextual bandits"
    - "online learning with graph structured feedback against adaptive adversaries"
    - "small loss bounds for online learning with partial information"
    - "sparsity variance and curvature in multi armed bandits"

Connected through hyperedge HE-2 (induced by source paper "connections between mirror descent thompson sampling and the information ratio"):
  with members:
    - "introduction to online convex optimization"
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "sparsity variance and curvature in multi armed bandits"
    - "online learning with feedback graphs without the graphs"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "towards optimal algorithms for prediction with expert advice"

Connected through hyperedge HE-3 (induced by source paper "thompson sampling for adversarial bit prediction"):
  with members:
    - "introduction to multi armed bandits"
    - "a tutorial on thompson sampling"
    - "towards optimal algorithms for prediction with expert advice"
    - "an information theoretic analysis of thompson sampling"
    - "further optimal regret bounds for thompson sampling"
    - "the best of both worlds stochastic and adversarial bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"

Connected through hyperedge HE-4 (induced by source paper "on first order bounds variance and gap dependent bounds for adversarial bandits"):
  with members:
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds"
    - "first order regret bounds for combinatorial semi bandits"

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)