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: "pure exploration of multi armed bandit under matroid constraints"
  Abstract: We study the pure exploration problem subject to a matroid constraint (Best-Basis) in a stochastic multi-armed bandit game. In a Best-Basis instance, we are given $n$ stochastic arms with unknown reward distributions, as well as a matroid $\mathcal{M}$ over the arms. Let the weight of an arm be the mean of its reward distribution. Our goal is to identify a basis of $\mathcal{M}$ with the maximum total weight, using as few samples as possible. #R##N#The problem is a significant generalization of the best arm identification problem and the top-$k$ arm identification problem, which have attracted significant attentions in recent years. We study both the exact and PAC versions of Best-Basis, and provide algorithms with nearly-optimal sample complexities for these versions. Our results generalize and/or improve on several previous results for the top-$k$ arm identification problem and the combinatorial pure exploration problem when the combinatorial constraint is a matroid.

Connected through hyperedge HE-1 (induced by source paper "polynomial time algorithms for multiple arm identification with full bandit feedback"):
  with members:
    - "pac identification of many good arms in stochastic multi armed bandits"
    - "nearly optimal sampling algorithms for combinatorial pure exploration"
    - "multiple play bandits in the position based model"
    - "approximation of the quadratic knapsack problem"
    - "on the optimal sample complexity for best arm identification"
    - "powers of tensors and fast matrix multiplication"
    - "best arm identification in linear bandits"
    - "multiple identifications in multi armed bandits"

Connected through hyperedge HE-2 (induced by source paper "polynomial time algorithms for combinatorial pure exploration with full bandit feedback"):
  with members:
    - "approximation of the quadratic knapsack problem"
    - "on the optimal sample complexity for best arm identification"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "exploring k out of top rho fraction of arms in stochastic bandits"):
  with members:
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "nearly optimal sampling algorithms for combinatorial pure exploration"):
  with members:
    - "nearly instance optimal sample complexity bounds for top k arm selection"
    - "towards instance optimal bounds for best arm identification"
    - "on the optimal sample complexity for best arm identification"
    - "active sequential hypothesis testing"

Connected through hyperedge HE-5 (induced by source paper "the simulator understanding adaptive sampling in the moderate confidence regime"):
  with members:
    - "nearly instance optimal sample complexity bounds for top k arm selection"
    - "on the optimal sample complexity for best arm identification"
    - "matroid bandits fast combinatorial optimization with learning"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-6 (induced by source paper "nearly instance optimal sample complexity bounds for top k arm selection"):
  with members:
    - "towards instance optimal bounds for best arm identification"
    - "on the optimal sample complexity for best arm identification"
    - "multiple identifications in multi armed bandits"

Connected through hyperedge HE-7 (induced by source paper "adaptive multiple arm identification"):
  with members:
    - "towards instance optimal bounds for best arm identification"

Connected through hyperedge HE-8 (induced by source paper "towards instance optimal bounds for best arm identification"):
  with members:
    - "nearly instance optimal sample complexity bounds for top k arm selection"
    - "on the optimal sample complexity for best arm identification"
    - "instance optimal geometric algorithms"
    - "multiple identifications in multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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)