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: "reducing dueling bandits to cardinal bandits"
  Abstract: We present algorithms for reducing the Dueling Bandits problem to the conventional (stochastic) Multi-Armed Bandits problem. The Dueling Bandits problem is an online model of learning with ordinal feedback of the form "A is preferred to B" (as opposed to cardinal feedback like "A has value 2.5"), giving it wide applicability in learning from implicit user feedback and revealed and stated preferences. In contrast to existing algorithms for the Dueling Bandits problem, our reductions -- named $\Doubler$, $\MultiSbm$ and $\DoubleSbm$ -- provide a generic schema for translating the extensive body of known results about conventional Multi-Armed Bandit algorithms to the Dueling Bandits setting. For $\Doubler$ and $\MultiSbm$ we prove regret upper bounds in both finite and infinite settings, and conjecture about the performance of $\DoubleSbm$ which empirically outperforms the other two as well as previous algorithms in our experiments. In addition, we provide the first almost optimal regret bound in terms of second order terms, such as the differences between the values of the arms.

Connected through hyperedge HE-1 (induced by source paper "how good is a two party election game"):
  with members:
    - "randomized social choice functions under metric preferences"

Connected through hyperedge HE-2 (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-3 (induced by source paper "klucb approach to copeland bandits"):
  with members:
    - "multi dueling bandits with dependent arms"
    - "double thompson sampling for dueling bandits"

Connected through hyperedge HE-4 (induced by source paper "tsallis inf an optimal algorithm for stochastic and adversarial bandits"):
  with members:
    - "connections between mirror descent thompson sampling and the information ratio"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "corralling a band of bandit algorithms"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "fighting bandits with a new kind of smoothness"

Connected through hyperedge HE-5 (induced by source paper "preference based online learning with dueling bandits a survey"):
  with members:
    - "multi dueling bandits with dependent arms"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "dueling bandits with dependent arms"
    - "double thompson sampling for dueling bandits"
    - "further optimal regret bounds for thompson sampling"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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"
    - "relative upper confidence bound for the k armed dueling bandit problem"

Connected through hyperedge HE-7 (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"
    - "contextual dueling bandits"
    - "learning to optimize via posterior sampling"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-8 (induced by source paper "dueling bandits with weak regret"):
  with members:
    - "copeland dueling bandits"
    - "relative upper confidence bound for the k armed dueling bandit problem"
    - "active ranking using pairwise comparisons"

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)