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 bayesian algorithms for best arm identification"
  Abstract: This paper considers the optimal adaptive allocation of measurement effort for identifying the best among a finite set of options or designs. An experimenter sequentially chooses designs to measure and observes noisy signals of their quality with the goal of confidently identifying the best design after a small number of measurements. This paper proposes three simple and intuitive Bayesian algorithms for adaptively allocating measurement effort, and formalizes a sense in which these seemingly naive rules are the best possible. One proposal is top-two probability sampling, which computes the two designs with the highest posterior probability of being optimal, and then randomizes to select among these two. One is a variant of top-two sampling which considers not only the probability a design is optimal, but the expected amount by which its quality exceeds that of other designs. The final algorithm is a modified version of Thompson sampling that is tailored for identifying the best design. We prove that these simple algorithms satisfy a sharp optimality property. In a frequentist setting where the true quality of the designs is fixed, one hopes the posterior definitively identifies the optimal design, in the sense that that the posterior probability assigned to the event that some other design is optimal converges to zero as measurements are collected. We show that under the proposed algorithms this convergence occurs at an exponential rate, and the corresponding exponent is the best possible among all allocation

Connected through hyperedge HE-1 (induced by source paper "fiesta fast identification of state of the art models using adaptive bandit algorithms"):
  with members:
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
    - "design challenges and misconceptions in neural sequence labeling"
    - "allennlp a deep semantic natural language processing platform"
    - "using j k fold cross validation to reduce variance when tuning nlp models"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"
    - "interactive attention networks for aspect level sentiment classification"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-2 (induced by source paper "fixed confidence guarantees for bayesian best arm identification"):
  with members:
    - "improving the expected improvement algorithm"
    - "learning the distribution with largest mean two bandit frameworks"

Connected through hyperedge HE-3 (induced by source paper "optimal delta correct best arm selection for general distributions"):
  with members:
    - "sample complexity of partition identification using multi armed bandits"

Connected through hyperedge HE-4 (induced by source paper "belman bayesian bandits on the belief reward manifold"):
  with members:
    - "regret of queueing bandits"
    - "an information theoretic analysis of thompson sampling"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-5 (induced by source paper "a tutorial on thompson sampling"):
  with members:
    - "an efficient bandit algorithm for realtime multivariate optimization"
    - "learning unknown markov decision processes a thompson sampling approach"
    - "time sensitive bandit learning and satisficing thompson sampling"
    - "thompson sampling for the mnl bandit"
    - "deep exploration via bootstrapped dqn"
    - "cascading bandits learning to rank in the cascade model"
    - "bayesian reinforcement learning a survey"
    - "learning to optimize via information directed sampling"

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)