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: "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.

Connected through hyperedge HE-1 (induced by source paper "regret minimization in stochastic contextual dueling bandits"):
  with members:
    - "combinatorial bandits with relative feedback"
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "multi dueling bandits with dependent arms"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "data driven rank breaking for efficient rank aggregation"
    - "double thompson sampling for dueling bandits"

Connected through hyperedge HE-2 (induced by source paper "simple algorithms for dueling bandits"):
  with members:
    - "a tutorial on thompson sampling"
    - "multi dueling bandits with dependent arms"
    - "double thompson sampling for dueling bandits"
    - "thompson sampling for budgeted multi armed bandits"
    - "contextual dueling bandits"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "relative upper confidence bound for the k armed dueling bandit problem"

Connected through hyperedge HE-3 (induced by source paper "dueling bandits with weak regret"):
  with members:
    - "reducing dueling bandits to cardinal bandits"
    - "relative upper confidence bound for the k armed dueling bandit problem"
    - "active ranking using pairwise comparisons"

Connected through hyperedge HE-4 (induced by source paper "multi dueling bandits and their application to online ranker evaluation"):
  with members:
    - "an improved multileaving algorithm for online ranker evaluation"
    - "contextual dueling bandits"
    - "reducing dueling bandits to cardinal bandits"
    - "relative upper confidence bound for the k armed dueling bandit problem"

Connected through hyperedge HE-5 (induced by source paper "decoy bandits dueling on a poset"):
  with members:
    - "reducing dueling bandits to cardinal bandits"
    - "relative upper confidence bound for the k armed dueling bandit problem"
    - "sorting and selection in posets"

Connected through hyperedge HE-6 (induced by source paper "contextual dueling bandits"):
  with members:
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "reducing dueling bandits to cardinal bandits"
    - "relative upper confidence bound for the k armed dueling bandit problem"
    - "efficient optimal learning for contextual bandits"
    - "contextual bandit algorithms with supervised learning guarantees"

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)