human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "smoothness adaptive stochastic bandits"):
  Abstract: We consider the problem of non-parametric multi-armed bandits with stochastic covariates, where a key factor in determining the complexity of the problem and in the design of effective policies is the smoothness of payoff functions. Previous work treats this problem when the smoothness of payoff functions are a priori known. In practical settings, however, the smoothness that characterizes the class of functions to which payoff functions belong is not known in advance, and misspecification of this smoothness may cause the performance of existing methods to severely deteriorate. In this work, we address the challenge of adapting to a priori unknown smoothness in the payoff functions. Our approach is based on the notion of \textit{self-similarity} that appears in the literature on adaptive non-parametric confidence intervals. We develop a procedure that infers a global smoothness parameter of the payoff functions based on collected observations, and establish that this procedure achieves rate-optimal performance up to logarithmic factors. We further extend this method in order to account for local complexity of the problem which depends on how smooth payoff functions are in different regions of the covariate space. We show that under reasonable assumptions on the way this smoothness changes over the covariate space, our method achieves significantly improved performance that is characterized by the local complexity of the problem as opposed to its global complexity.

Includes members:
  Member "the k nearest neighbour ucb algorithm for multi armed bandits with covariates":
    also connected through other hyperedges:
      (none)
  Member "thompson sampling for contextual bandits with linear payoffs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "regret analysis of stochastic and nonstochastic multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "efficient optimal learning for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")

Question: Please tell me which class the source paper 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)