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: "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.

Connected through hyperedge HE-1 (induced by source paper "causal induction from visual observations for goal directed tasks"):
  with members:
    - "gradient based neural dag learning"
    - "environment probing interaction policies"
    - "dag gnn dag structure learning with graph neural networks"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "causal reasoning from meta reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "learning plannable representations with causal infogan"
    - "woulda coulda shoulda counterfactually guided policy search"

Connected through hyperedge HE-2 (induced by source paper "expanding the active inference landscape more intrinsic motivations in the perception action loop"):
  with members:
    - "new and surprising ways to be mean adversarial npcs with coupled empowerment minimisation"
    - "accelerating empowerment computation with uct tree search"
    - "universal reinforcement learning algorithms survey and experiments"
    - "minimum information lqg control part ii retentive controllers"
    - "nonparametric general reinforcement learning"
    - "variational intrinsic control"
    - "the umwelt of an embodied agent a measure theoretic definition"
    - "empowerment an introduction"

Connected through hyperedge HE-3 (induced by source paper "playing against nature causal discovery for decision making under uncertainty"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "towards deep symbolic reinforcement learning"
    - "building machines that learn and think like people"
    - "on the optimal sample complexity for best arm identification"
    - "taming the monster a fast and simple algorithm for contextual bandits"

Connected through hyperedge HE-4 (induced by source paper "planning with information processing constraints and model uncertainty in markov decision processes"):
  with members:
    - "taming the noise in reinforcement learning via soft updates"
    - "risk sensitive and robust decision making a cvar optimization approach"
    - "g learning taming the noise in reinforcement learning via soft updates"
    - "risk sensitive reinforcement learning"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "risk sensitive path integral control"
    - "a bayesian rule for adaptive control based on causal interventions"
    - "a minimum relative entropy principle for learning and acting"

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.AI(Artificial Intelligence)