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: "contextual dueling bandits"
  Abstract: We consider the problem of learning to choose actions using contextual information when provided with limited feedback in the form of relative pairwise comparisons. We study this problem in the dueling-bandits framework of Yue et al. (2009), which we extend to incorporate context. Roughly, the learner's goal is to find the best policy, or way of behaving, in some space of policies, although "best" is not always so clearly defined. Here, we propose a new and natural solution concept, rooted in game theory, called a von Neumann winner, a randomized policy that beats or ties every other policy. We show that this notion overcomes important limitations of existing solutions, particularly the Condorcet winner which has typically been used in the past, but which requires strong and often unrealistic assumptions. We then present three efficient algorithms for online learning in our setting, and for approximating a von Neumann winner from batch-like data. The first of these algorithms achieves particularly low regret, even when data is adversarial, although its time and space requirements are linear in the size of the policy space. The other two algorithms require time and space only logarithmic in the size of the policy space when provided access to an oracle for solving classification problems on the space.

Connected through hyperedge HE-1 (induced by source paper "online preselection with context information under the plackett luce model"):
  with members:
    - "preselection bandits under the plackett luce model"
    - "regret minimisation in multinomial logit bandits"
    - "multinomial logit bandit with linear utility functions"
    - "preference based online learning with dueling bandits a survey"
    - "active ranking with subset wise preferences"
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "automated algorithm selection survey and perspectives"
    - "pac battling bandits in the plackett luce model"

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"
    - "copeland 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 posterior sampling for preference based reinforcement learning"):
  with members:
    - "stagewise safe bayesian optimization with gaussian processes"
    - "multi dueling bandits with dependent arms"
    - "learning dynamic robot to human object handover from human feedback"
    - "concrete problems in ai safety"

Connected through hyperedge HE-4 (induced by source paper "active ranking with subset wise preferences"):
  with members:
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "assortment optimisation under a general discrete choice model a tight analysis of revenue ordered assortments"
    - "data driven rank breaking for efficient rank aggregation"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "random utility theory for social choice"
    - "active ranking using pairwise comparisons"
    - "rank aggregation via nuclear norm minimization"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-5 (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"
    - "reducing dueling bandits to cardinal bandits"
    - "relative upper confidence bound for the k armed dueling bandit problem"

Connected through hyperedge HE-6 (induced by source paper "multi dueling bandits with dependent arms"):
  with members:
    - "a relative exponential weighing algorithm for adversarial utility based dueling bandits"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "double thompson sampling for dueling bandits"
    - "reducing dueling bandits to cardinal bandits"
    - "learning to optimize via posterior sampling"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-7 (induced by source paper "from preference based to multiobjective sequential decision making"):
  with members:
    - "a survey of multi objective sequential decision making"
    - "approximation of lorenz optimal solutions in multiobjective markov decision processes"
    - "april active preference learning based reinforcement learning"
    - "implementation and comparison of solution methods for decision processes with non markovian rewards"

Connected through hyperedge HE-8 (induced by source paper "multi dueling bandits and their application to online ranker evaluation"):
  with members:
    - "an improved multileaving algorithm for online ranker evaluation"
    - "copeland dueling bandits"
    - "reducing dueling bandits to cardinal bandits"
    - "relative upper confidence bound for the k armed dueling bandit problem"

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)