human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "global bandits"):
  Abstract: Multi-armed bandits (MAB) model sequential decision making problems, in which a learner sequentially chooses arms with unknown reward distributions in order to maximize its cumulative reward. Most of the prior work on MAB assumes that the reward distributions of each arm are independent. But in a wide variety of decision problems -- from drug dosage to dynamic pricing -- the expected rewards of different arms are correlated, so that selecting one arm provides information about the expected rewards of other arms as well. We propose and analyze a class of models of such decision problems, which we call {\em global bandits}. In the case in which rewards of all arms are deterministic functions of a single unknown parameter, we construct a greedy policy that achieves {\em bounded regret}, with a bound that depends on the single true parameter of the problem. Hence, this policy selects suboptimal arms only finitely many times with probability one. For this case we also obtain a bound on regret that is {\em independent of the true parameter}; this bound is sub-linear, with an exponent that depends on the informativeness of the arms. We also propose a variant of the greedy policy that achieves $\tilde{\mathcal{O}}(\sqrt{T})$ worst-case and $\mathcal{O}(1)$ parameter dependent regret. Finally, we perform experiments on dynamic pricing and show that the proposed algorithms achieve significant gains with respect to the well-known benchmarks.

Includes members:
  Member "bounded regret for finite armed structured bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regime switching bandits")
  Member "an information theoretic analysis of thompson sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "distributed online learning via cooperative contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense")
  Member "thompson sampling for contextual bandits with linear payoffs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "regret analysis of stochastic and nonstochastic multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "from bandits to experts on the value of side observations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "an optimal algorithm for linear bandits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the bayesian linear information filtering problem")
  Member "a contextual bandit approach to personalized news article recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

Question: Please tell me which class the source paper 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)