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: "multiple identifications in multi armed bandits"
  Abstract: We study the problem of identifying the top $m$ arms in a multi-armed bandit game. Our proposed solution relies on a new algorithm based on successive rejects of the seemingly bad arms, and successive accepts of the good ones. This algorithmic contribution allows to tackle other multiple identifications settings that were previously out of reach. In particular we show that this idea of successive accepts and rejects applies to the multi-bandit best arm identification problem.

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"
    - "best arm identification in linear bandits"

Connected through hyperedge HE-2 (induced by source paper "collaborative learning with limited interaction tight bounds for distributed exploration in multi armed bandits"):
  with members:
    - "improved algorithms for collaborative pac learning"
    - "tight bounds for collaborative pac learning via multiplicative weights"
    - "adaptive multiple arm identification"
    - "towards instance optimal bounds for best arm identification"
    - "delay and cooperation in nonstochastic bandits"
    - "multi player bandits a musical chairs approach"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "distributed exploration in multi armed bandits"

Connected through hyperedge HE-3 (induced by source paper "thresholding bandit problem with both duels and pulls"):
  with members:
    - "active classification with comparison queries"
    - "thresholding bandits with augmented ucb"
    - "adaptive multiple arm identification"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "statistical decision making for optimal budget allocation in crowd labeling"

Connected through hyperedge HE-4 (induced by source paper "making the cut a bandit based approach to tiered interviewing"):
  with members:
    - "a voting based system for ethical decision making"
    - "the diverse cohort selection problem"
    - "learning to hire teams"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-5 (induced by source paper "sic mmab synchronisation involves communication in multiplayer multi armed bandits"):
  with members:
    - "an optimal algorithm in multiplayer multi armed bandits"
    - "non stochastic multi player multi armed bandits optimal rate with collision information sublinear without"
    - "multiplayer bandits without observing collision information"
    - "multi user communication networks a coordinated multi armed bandit approach"
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"

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"
    - "pure exploration of multi armed bandit under matroid constraints"
    - "on the optimal sample complexity for best arm identification"

Connected through hyperedge HE-7 (induced by source paper "competitive analysis of the top k ranking problem"):
  with members:
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "adversarial top k ranking"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "simulating noisy channel interaction"
    - "rank centrality ranking from pair wise comparisons"
    - "active ranking using pairwise comparisons"
    - "noisy sorting without resampling"

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"
    - "pure exploration of multi armed bandit under matroid constraints"
    - "on the optimal sample complexity for best arm identification"
    - "instance optimal geometric algorithms"
    - "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)