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: "a bayesian rule for adaptive control based on causal interventions"
  Abstract: Explaining adaptive behavior is a central problem in artificial intelligence research. Here we formalize adaptive agents as mixture distributions over sequences of inputs and outputs (I/O). Each distribution of the mixture constitutes a `possible world', but the agent does not know which of the possible worlds it is actually facing. The problem is to adapt the I/O stream in a way that is compatible with the true world. A natural measure of adaptation can be obtained by the Kullback-Leibler (KL) divergence between the I/O distribution of the true world and the I/O distribution expected by the agent that is uncertain about possible worlds. In the case of pure input streams, the Bayesian mixture provides a well-known solution for this problem. We show, however, that in the case of I/O streams this solution breaks down, because outputs are issued by the agent itself and require a different probabilistic syntax as provided by intervention calculus. Based on this calculus, we obtain a Bayesian control rule that allows modeling adaptive behavior with mixture distributions over I/O streams. This rule might allow for a novel approach to adaptive control based on a minimum KL-principle.

Connected through hyperedge HE-1 (induced by source paper "stacked thompson bandits"):
  with members:
    - "qos aware multi armed bandits"
    - "algorithms for multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "statistical model checking an overview"

Connected through hyperedge HE-2 (induced by source paper "scalable multiagent coordination with distributed online open loop planning"):
  with members:
    - "qos aware multi armed bandits"
    - "deep learning in neural networks"
    - "algorithms for multi armed bandit problems"

Connected through hyperedge HE-3 (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"
    - "generalized thompson sampling for sequential decision making and causal inference"
    - "risk sensitive reinforcement learning"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "risk sensitive path integral control"
    - "a minimum relative entropy principle for learning and acting"

Connected through hyperedge HE-4 (induced by source paper "generalized thompson sampling for sequential decision making and causal inference"):
  with members:
    - "learning to optimize via posterior sampling"
    - "monte carlo utility estimates for bayesian reinforcement learning"
    - "thompson sampling in switching environments with bayesian online change point detection"
    - "a bayesian sampling approach to exploration in reinforcement learning"
    - "information utility bounded rationality"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a minimum relative entropy principle for learning and acting"
    - "time space and energy in reversible computing"

Connected through hyperedge HE-5 (induced by source paper "an axiomatic formalization of bounded rationality based on a utility information equivalence"):
  with members:
    - "a minimum relative entropy principle for learning and acting"

Connected through hyperedge HE-6 (induced by source paper "a minimum relative entropy principle for learning and acting"):
  with members:
    - "model based bayesian exploration"
    - "defensive universal learning with experts"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

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)