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 "generalized thompson sampling for sequential decision making and causal inference"):
  Abstract: Purpose#R##N#Sampling an action according to the probability that the action is believed to be the optimal one is sometimes called Thompson sampling.

Includes members:
  Member "learning to optimize via posterior sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "monte carlo utility estimates for bayesian reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bayesian reinforcement learning via deep sparse sampling")
  Member "thompson sampling in switching environments with bayesian online change point detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "better boosting with bandits for online learning")
  Member "a bayesian sampling approach to exploration in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "worst case regret bounds for exploration via randomized value functions")
  Member "information utility bounded rationality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "information asymmetry in kl regularized rl")
  Member "analysis of thompson sampling for the multi armed bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "a bayesian rule for adaptive control based on causal interventions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stacked thompson bandits")
  Member "a minimum relative entropy principle for learning and acting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient exploration of zero sum stochastic games")

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.AI(Artificial Intelligence)