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 "randomized exploration in generalized linear bandits"):
  Abstract: We study two randomized algorithms for generalized linear bandits, GLM-TSL and GLM-FPL. GLM-TSL samples a generalized linear model (GLM) from the Laplace approximation to the posterior distribution. GLM-FPL, a new algorithm proposed in this work, fits a GLM to a randomly perturbed history of past rewards. We prove a $\tilde{O}(d \sqrt{n} + d^2)$ upper bound on the $n$-round regret of GLM-TSL, where $d$ is the number of features. This is the first regret bound of a Thompson sampling-like algorithm in GLM bandits where the leading term is $\tilde{O}(d \sqrt{n})$. We apply both GLM-TSL and GLM-FPL to logistic and neural network bandits, and show that they perform well empirically. In more complex models, GLM-FPL is significantly faster. Our results showcase the role of randomization, beyond posterior sampling, in exploration.

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 "provably optimal algorithms for generalized linear contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "a tutorial on thompson sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "customized nonlinear bandits for online response selection in neural conversation models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a repository of conversational datasets")
  Member "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "further optimal regret bounds for thompson sampling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "delay adaptive learning in generalized linear contextual bandits")
  Member "thompson sampling for contextual bandits with linear payoffs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural contextual bandits with ucb based exploration")

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)