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 "copeland dueling bandits"):
  Abstract: A version of the dueling bandit problem is addressed in which a Condorcet winner may not exist. Two algorithms are proposed that instead seek to minimize regret with respect to the Copeland winner, which, unlike the Condorcet winner, is guaranteed to exist. The first, Copeland Confidence Bound (CCB), is designed for small numbers of arms, while the second, Scalable Copeland Bandits (SCB), works better for large-scale problems. We provide theoretical results bounding the regret accumulated by CCB and SCB, both substantially improving existing results. Such existing results either offer bounds of the form $O(K \log T)$ but require restrictive assumptions, or offer bounds of the form $O(K^2 \log T)$ without requiring such assumptions. Our results offer the best of both worlds: $O(K \log T)$ bounds without restrictive assumptions.

Includes members:
  Member "contextual dueling bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online preselection with context information under the plackett luce model")
  Member "reducing dueling bandits to cardinal bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how good is a two party election game")
  Member "relative upper confidence bound for the k armed dueling bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "exponential regret bounds for gaussian process bandits with deterministic observations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on hyper parameter tuning for stochastic optimization algorithms")
  Member "an adaptive algorithm for finite stochastic partial monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dcm bandits learning to rank with multiple clicks")
  Member "x armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on thompson sampling for smoother than lipschitz bandits")
  Member "multi armed bandits in metric spaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural contextual bandits with ucb based exploration")

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)