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 "feedback graph regret bounds for thompson sampling and ucb"):
  Abstract: We study the stochastic multi-armed bandit problem with the graph-based feedback structure introduced by Mannor and Shamir. We analyze the performance of the two most prominent stochastic bandit algorithms, Thompson Sampling and Upper Confidence Bound (UCB), in the graph-based feedback setting. We show that these algorithms achieve regret guarantees that combine the graph structure and the gaps between the means of the arm distributions. Surprisingly this holds despite the fact that these algorithms do not explicitly use the graph structure to select arms; they observe the additional feedback but do not explore based on it. Towards this result we introduce a "layering technique" highlighting the commonalities in the two algorithms.

Includes members:
  Member "information directed sampling for stochastic bandits with graph feedback":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "thompson sampling for combinatorial network optimization in unknown environments")
  Member "thompson sampling for stochastic bandits with graph feedback":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bandits with feedback graphs and switching costs")
  Member "small loss bounds for online learning with partial information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bandits with adversarial scaling")
  Member "reward maximization under uncertainty leveraging side observations on networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "waiting but not aging age of information and utility optimization under the pull model")
  Member "online learning with feedback graphs without the graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "online learning with feedback graphs beyond bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "nonstochastic multi armed bandits with graph structured feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "learning to optimize via information directed sampling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "making sense of reinforcement learning and probabilistic inference")

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)