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 minimum relative entropy principle for learning and acting"
  Abstract: This paper proposes a method to construct an adaptive agent that is universal with respect to a given class of experts, where each expert is an agent that has been designed specifically for a particular environment. This adaptive control problem is formalized as the problem of minimizing the relative entropy of the adaptive agent from the expert that is most suitable for the unknown environment. If the agent is a passive observer, then the optimal solution is the well-known Bayesian predictor. However, if the agent is active, then its past actions need to be treated as causal interventions on the I/O stream rather than normal probability conditions. Here it is shown that the solution to this new variational problem is given by a stochastic controller called the Bayesian control rule, which implements adaptive behavior as a mixture of experts. Furthermore, it is shown that under mild assumptions, the Bayesian control rule converges to the control law of the most suitable expert.

Connected through hyperedge HE-1 (induced by source paper "efficient exploration of zero sum stochastic games"):
  with members:
    - "the uncertainty bellman equation and exploration"
    - "a distributional perspective on reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "learning to optimize via posterior sampling"

Connected through hyperedge HE-2 (induced by source paper "meta learning of sequential strategies"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "causal reasoning from meta reinforcement learning"
    - "conditional neural processes"
    - "sample efficient adaptive text to speech"
    - "modeling friends and foes"

Connected through hyperedge HE-3 (induced by source paper "bounded rational decision making from elementary computations that reduce uncertainty"):
  with members:
    - "systems of bounded rational agents with information theoretic constraints"
    - "bounded rational decision making with adaptive neural network priors"
    - "a short characterization of relative entropy"
    - "regularised deep reinforcement learning with guaranteed convergence"
    - "an information theoretic optimality principle for deep reinforcement learning"
    - "information measures the curious case of the binary alphabet"
    - "provably bounded optimal agents"

Connected through hyperedge HE-4 (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"
    - "generalized thompson sampling for sequential decision making and causal inference"

Connected through hyperedge HE-5 (induced by source paper "information gain computation"):
  with members:
    - "info clustering a mathematical theory for data clustering"
    - "contextual bandit algorithms with supervised learning guarantees"

Connected through hyperedge HE-6 (induced by source paper "thompson sampling is asymptotically optimal in general environments"):
  with members:
    - "bad universal priors and notions of optimality"
    - "a bayesian sampling approach to exploration in reinforcement learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "asymptotically optimal agents"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "universal intelligence a definition of machine intelligence"
    - "general discounting versus average reward"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

Connected through hyperedge HE-7 (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 bayesian rule for adaptive control based on causal interventions"

Connected through hyperedge HE-8 (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 bayesian rule for adaptive control based on causal interventions"
    - "time space and energy in reversible computing"

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)