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: "multiarmed bandits with limited expert advice"
  Abstract: We solve the COLT 2013 open problem of \citet{SCB} on minimizing regret in the setting of advice-efficient multiarmed bandits with expert advice. We give an algorithm for the setting of K arms and N experts out of which we are allowed to query and use only M experts' advices in each round, which has a regret bound of \tilde{O}\bigP{\sqrt{\frac{\min\{K, M\} N}{M} T}} after T rounds. We also prove that any algorithm for this problem must have expected regret at least \tilde{\Omega}\bigP{\sqrt{\frac{\min\{K, M\} N}{M}T}}, thus showing that our upper bound is nearly tight.

Connected through hyperedge HE-1 (induced by source paper "adaptation to easy data in prediction with limited advice"):
  with members:
    - "more adaptive algorithms for adversarial bandits"
    - "bandit regret scaling with the effective loss range"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "second order quantile methods for experts and combinatorial games"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "the best of both worlds stochastic and adversarial bandits"

Connected through hyperedge HE-2 (induced by source paper "learning to use learners advice"):
  with members:
    - "corralling a band of bandit algorithms"
    - "fast convergence of regularized learning in games"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandit algorithms with supervised learning guarantees"

Connected through hyperedge HE-3 (induced by source paper "collaborative learning of stochastic bandits over a social network"):
  with members:
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "delay and cooperation in nonstochastic bandits"
    - "stochastic bandits on a social network collaborative learning with local information sharing"
    - "regret vs communication distributed stochastic multi armed bandits and beyond"
    - "distributed multi armed bandits regret vs communication"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "online learning with feedback graphs beyond bandits"
    - "the domination number of on line social networks and random geometric graphs"

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)