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 "new insights into bootstrapping for bandits"):
  Abstract: We investigate the use of bootstrapping in the bandit setting. We first show that the commonly used non-parametric bootstrapping (NPB) procedure can be provably inefficient and establish a near-linear lower bound on the regret incurred by it under the bandit model with Bernoulli rewards. We show that NPB with an appropriate amount of forced exploration can result in sub-linear albeit sub-optimal regret. As an alternative to NPB, we propose a weighted bootstrapping (WB) procedure. For Bernoulli rewards, WB with multiplicative exponential weights is mathematically equivalent to Thompson sampling (TS) and results in near-optimal regret bounds. Similarly, in the bandit setting with Gaussian rewards, we show that WB with additive Gaussian weights achieves near-optimal regret. Beyond these special cases, we show that WB leads to better empirical performance than TS for several reward distributions bounded on $[0,1]$. For the contextual bandit setting, we give practical guidelines that make bootstrapping simple and efficient to implement and result in good empirical performance on real-world datasets.

Includes members:
  Member "provably optimal algorithms for generalized linear contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "a practical method for solving contextual bandit problems using decision trees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decision trees for decision making under the predict then optimize framework")
  Member "deep exploration via bootstrapped dqn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "online stochastic linear optimization under one bit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "old dog learns new tricks randomized ucb for bandit problems")
  Member "taming the monster a fast and simple algorithm for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "thompson sampling with the online bootstrap":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "budgeted policy learning for task oriented dialogue systems")
  Member "api design for machine learning software experiences from the scikit learn project":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "further optimal regret bounds for thompson sampling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "delay adaptive learning in generalized linear contextual bandits")

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)