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: "multi dueling bandits with dependent arms"
  Abstract: The dueling bandits problem is an online learning framework for learning from pairwise preference feedback, and is particularly well-suited for modeling settings that elicit subjective or implicit human feedback. In this paper, we study the problem of multi-dueling bandits with dependent arms, which extends the original dueling bandits setting by simultaneously dueling multiple arms as well as modeling dependencies between arms. These extensions capture key characteristics found in many real-world applications, and allow for the opportunity to develop significantly more efficient algorithms than were possible in the original setting. We propose the \selfsparring algorithm, which reduces the multi-dueling bandits problem to a conventional bandit setting that can be solved using a stochastic bandit algorithm such as Thompson Sampling, and can naturally model dependencies using a Gaussian process prior. We present a no-regret analysis for multi-dueling setting, and demonstrate the effectiveness of our algorithm empirically on a wide range of simulation settings.

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 and their application to online ranker evaluation"
    - "data driven rank breaking for efficient rank aggregation"
    - "double thompson sampling for dueling bandits"
    - "copeland 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"
    - "competitive analysis of the top k ranking problem"
    - "data driven rank breaking for efficient rank aggregation"
    - "relative upper confidence bound for the k armed dueling bandit problem"

Connected through hyperedge HE-3 (induced by source paper "human preference based learning for high dimensional optimization of exoskeleton walking gaits"):
  with members:
    - "towards variable assistance for lower body exoskeletons"
    - "preference based learning for exoskeleton gait optimization"
    - "online trajectory planning through combined trajectory optimization and function approximation application to the exoskeleton atalante"
    - "feedback control of an exoskeleton for paraplegics toward robustly stable hands free dynamic walking"
    - "online structured prediction via coactive learning"

Connected through hyperedge HE-4 (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"
    - "thompson sampling for the mnl bandit"
    - "dcm bandits learning to rank with multiple clicks"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "data driven rank breaking for efficient rank aggregation"

Connected through hyperedge HE-5 (induced by source paper "simple algorithms for dueling bandits"):
  with members:
    - "a tutorial on thompson sampling"
    - "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"
    - "relative upper confidence bound for the k armed dueling bandit problem"

Connected through hyperedge HE-6 (induced by source paper "preselection bandits under the plackett luce model"):
  with members:
    - "combinatorial bandits with relative feedback"
    - "regret minimisation in multinomial logit bandits"
    - "preference based online learning with dueling bandits a survey"
    - "automated algorithm selection survey and perspectives"
    - "pac battling bandits in the plackett luce model"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "thompson sampling for the mnl bandit"
    - "double thompson sampling for dueling bandits"

Connected through hyperedge HE-7 (induced by source paper "from pac to instance optimal sample complexity in the plackett luce model"):
  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"
    - "competitive analysis of the top k ranking problem"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "data driven rank breaking for efficient rank aggregation"
    - "noisy sorting without resampling"

Connected through hyperedge HE-8 (induced by source paper "zeroth order non convex optimization with dueling choice bandits"):
  with members:
    - "active classification with comparison queries"
    - "hyperparameter optimization a spectral approach"
    - "on kernelized multi armed bandits"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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)