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 "garbage in reward out bootstrapping exploration in multi armed bandits"):
  Abstract: We propose a bandit algorithm that explores by randomizing its history of rewards. Specifically, it pulls the arm with the highest mean reward in a non-parametric bootstrap sample of its history with pseudo rewards. We design the pseudo rewards such that the bootstrap mean is optimistic with a sufficiently high probability. We call our algorithm Giro, which stands for garbage in, reward out. We analyze Giro in a Bernoulli bandit and derive a $O(K \Delta^{-1} \log n)$ bound on its $n$-round regret, where $\Delta$ is the difference in the expected rewards of the optimal and the best suboptimal arms, and $K$ is the number of arms. The main advantage of our exploration design is that it easily generalizes to structured problems. To show this, we propose contextual Giro with an arbitrary reward generalization model. We evaluate Giro and its contextual variant on multiple synthetic and real-world problems, and observe that it performs well.

Includes members:
  Member "perturbed history exploration in stochastic multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accelerated learning from recommender systems using multi armed bandit")
  Member "perturbed history exploration in stochastic linear bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "old dog learns new tricks randomized ucb for bandit problems")
  Member "new insights into bootstrapping for bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "provably optimal algorithms for generalized linear contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "decision trees for decision making under the predict then optimize framework")
  Member "online stochastic linear optimization under one bit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (induced by source paper "budgeted policy learning for task oriented dialogue systems")

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)