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: "simple regret for infinitely many armed bandits"
  Abstract: We consider a stochastic bandit problem with infinitely many arms. In this setting, the learner has no chance of trying all the arms even once and has to dedicate its limited number of samples only to a certain number of arms. All previous algorithms for this setting were designed for minimizing the cumulative regret of the learner. In this paper, we propose an algorithm aiming at minimizing the simple regret. As in the cumulative regret setting of infinitely many armed bandits, the rate of the simple regret will depend on a parameter $\beta$ characterizing the distribution of the near-optimal arms. We prove that depending on $\beta$, our algorithm is minimax optimal either up to a multiplicative constant or up to a $\log(n)$ factor. We also provide extensions to several important cases: when $\beta$ is unknown, in a natural setting where the near-optimal arms have a small variance, and in the case of unknown time horizon.

Connected through hyperedge HE-1 (induced by source paper "ballooning multi armed bandits"):
  with members:
    - "introduction to multi armed bandits"
    - "a tutorial on thompson sampling"
    - "the myopia of crowds a study of collective evaluation on stack exchange"
    - "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-2 (induced by source paper "pac identification of many good arms in stochastic multi armed bandits"):
  with members:
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (induced by source paper "hyperband a novel bandit based approach to hyperparameter optimization"):
  with members:
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "thoughts on massively scalable gaussian processes"
    - "least squares revisited scalable approaches for multi class prediction"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "convolutional neural networks applied to house numbers digit classification"
    - "fast cross validation via sequential testing"
    - "efficient multi start strategies for local search algorithms"

Connected through hyperedge HE-4 (induced by source paper "on the detection of mixture distributions with applications to the most biased coin problem"):
  with members:
    - "clamshell speeding up crowds for low latency data labeling"

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)