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: "best arm identification in linear bandits"
  Abstract: We study the best-arm identification problem in linear bandit, where the rewards of the arms depend linearly on an unknown parameter $\theta^*$ and the objective is to return the arm with the largest reward. We characterize the complexity of the problem and introduce sample allocation strategies that pull arms to identify the best arm with a fixed confidence, while minimizing the sample budget. In particular, we show the importance of exploiting the global linear structure to improve the estimate of the reward of near-optimal arms. We analyze the proposed strategies and compare their empirical performance. Finally, as a by-product of our analysis, we point out the connection to the $G$-optimality criterion used in optimal experimental design.

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"
    - "pure exploration of multi armed bandit under matroid constraints"
    - "approximation of the quadratic knapsack problem"
    - "on the optimal sample complexity for best arm identification"
    - "powers of tensors and fast matrix multiplication"
    - "multiple identifications in multi armed bandits"

Connected through hyperedge HE-2 (induced by source paper "sublinear optimal policy value estimation in contextual bandits"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "estimating learnability in the sublinear data regime"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (induced by source paper "sequential mode estimation with oracle queries"):
  with members:
    - "a theoretical analysis of first heuristics of crowdsourced entity resolution"
    - "the simulator understanding adaptive sampling in the moderate confidence regime"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "best arm identification in generalized linear bandits"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "learning to optimize via posterior sampling"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-5 (induced by source paper "towards optimal and efficient best arm identification in linear bandits"):
  with members:
    - "contextual bandits with latent confounders an nmf approach"

Connected through hyperedge HE-6 (induced by source paper "decentralized exploration in multi armed bandits"):
  with members:
    - "corrupt bandits for preserving local privacy"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "concurrent bandits and cognitive radio networks"
    - "distributed exploration in multi armed bandits"
    - "calibrating data to sensitivity in private data analysis"
    - "distributed non stochastic experts"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-7 (induced by source paper "network of bandits"):
  with members:
    - "distributed exploration in multi armed bandits"
    - "distributed non stochastic experts"
    - "efficient optimal learning for contextual bandits"

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)