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: "smoothness adaptive contextual bandits"
  Abstract: We study a non-parametric multi-armed bandit problem with stochastic covariates, where a key driver of complexity is the smoothness with which the payoff functions vary with covariates. Previous studies have derived minimax-optimal algorithms in cases where it is a priori known how smooth the payoff functions are. In practice, however, advance information about the smoothness of payoff functions is typically not available, and misspecification of smoothness may severely deteriorate the performance of existing methods. In this work, we consider a framework where the smoothness is not known a priori, and study when and how algorithms may adapt to unknown smoothness. First, we establish that, in general, designing bandit algorithms that adapt to the unknown smoothness of payoff functions is impossible. We overcome this impossibility result by leveraging the notion of self-similarity, a concept from the statistics literature that is traditionally invoked to enable adaptive confidence intervals. Under a self-similarity assumption, we develop a policy for inferring the smoothness of the payoff functions using observations that are collected throughout the decision-making process, and establish that this policy matches (up to a logarithmic scale) the regret rate that is achievable when smoothness is known a priori. Finally, we extend our method to account for local notions of smoothness and show that, under reasonable assumptions, our method achieves performance characterized by the local complexity of the problem as opposed to its global complexity.

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)