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: "relative upper confidence bound for the k armed dueling bandit problem"
  Abstract: This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms. Our approach extends the Upper Confidence Bound algorithm to the relative setting by using estimates of the pairwise probabilities to select a promising arm and applying Upper Confidence Bound with the winner as a benchmark. We prove a finite-time regret bound of order O(log t). In addition, our empirical results using real data from an information retrieval application show that it greatly outperforms the state of the art.

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 "best item learning in random utility models with subset choices"):
  with members:
    - "active ranking with subset wise preferences"
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "pac battling bandits in the plackett luce model"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "multi dueling bandits with dependent arms"
    - "competitive analysis of the top k ranking problem"
    - "data driven rank breaking for efficient rank aggregation"

Connected through hyperedge HE-3 (induced by source paper "combinatorial bandits with relative feedback"):
  with members:
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "pac battling bandits in the plackett luce model"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "multi dueling bandits with dependent arms"
    - "thompson sampling for the mnl bandit"
    - "dcm bandits learning to rank with multiple clicks"
    - "multi dueling bandits and their application to online ranker evaluation"

Connected through hyperedge HE-4 (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"
    - "copeland dueling bandits"
    - "contextual dueling bandits"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"

Connected through hyperedge HE-5 (induced by source paper "ordinal monte carlo tree search"):
  with members:
    - "preference based monte carlo tree search"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"

Connected through hyperedge HE-6 (induced by source paper "merge double thompson sampling for large scale online ranker evaluation"):
  with members:
    - "preference based online learning with dueling bandits a survey"
    - "multi dueling bandits with dependent arms"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "double thompson sampling for dueling bandits"
    - "contextual dueling bandits"
    - "reducing dueling bandits to cardinal bandits"

Connected through hyperedge HE-7 (induced by source paper "pac battling bandits in the plackett luce model"):
  with members:
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "budget constrained multi armed bandits with multiple plays"
    - "the simulator understanding adaptive sampling in the moderate confidence regime"
    - "competitive analysis of the top k ranking problem"
    - "data driven rank breaking for efficient rank aggregation"
    - "spectral mle top k rank aggregation from pairwise comparisons"

Connected through hyperedge HE-8 (induced by source paper "factored bandits"):
  with members:
    - "bernoulli rank 1 bandits for click feedback"
    - "stochastic rank 1 bandits"
    - "double thompson sampling for dueling bandits"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"

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)