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: "adaptive multiple arm identification"
  Abstract: We study the problem of selecting $K$ arms with the highest expected rewards in a stochastic $n$-armed bandit game. This problem has a wide range of applications, e.g., A/B testing, crowdsourcing, simulation optimization. Our goal is to develop a PAC algorithm, which, with probability at least $1-\delta$, identifies a set of $K$ arms with the aggregate regret at most $\epsilon$. The notion of aggregate regret for multiple-arm identification was first introduced in \cite{Zhou:14} , which is defined as the difference of the averaged expected rewards between the selected set of arms and the best $K$ arms. In contrast to \cite{Zhou:14} that only provides instance-independent sample complexity, we introduce a new hardness parameter for characterizing the difficulty of any given instance. We further develop two algorithms and establish the corresponding sample complexity in terms of this hardness parameter. The derived sample complexity can be significantly smaller than state-of-the-art results for a large class of instances and matches the instance-independent lower bound upto a $\log(\epsilon^{-1})$ factor in the worst case. We also prove a lower bound result showing that the extra $\log(\epsilon^{-1})$ is necessary for instance-dependent algorithms using the introduced hardness parameter.

Connected through hyperedge HE-1 (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"
    - "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"
    - "multiple identifications in multi armed bandits"

Connected through hyperedge HE-2 (induced by source paper "thresholding bandit problem with both duels and pulls"):
  with members:
    - "active classification with comparison queries"
    - "thresholding bandits with augmented ucb"
    - "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"
    - "multiple identifications in multi armed 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)