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: "information utility bounded rationality"
  Abstract: Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here we employ an axiomatic framework for bounded rational decision-making based on a thermodynamic interpretation of resource costs as information costs. This leads to a variational "free utility" principle akin to thermodynamical free energy that trades off utility and information costs. We show that bounded optimal control solutions can be derived from this variational principle, which leads in general to stochastic policies. Furthermore, we show that risk-sensitive and robust (minimax) control schemes fall out naturally from this framework if the environment is considered as a bounded rational and perfectly rational opponent, respectively. When resource costs are ignored, the maximum expected utility principle is recovered.

Connected through hyperedge HE-1 (induced by source paper "information asymmetry in kl regularized rl"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "kickstarting deep reinforcement learning"
    - "mix match agent curricula for reinforcement learning"
    - "equivalence between policy gradients and soft q learning"
    - "deep mutual learning"
    - "trust pcl an off policy trust region method for continuous control"
    - "fixing a broken elbo"

Connected through hyperedge HE-2 (induced by source paper "q search trees an information theoretic approach towards hierarchical abstractions for agents with computational limitations"):
  with members:
    - "hierarchical state abstractions for decision making problems with computational constraints"
    - "quantization of binary input discrete memoryless channels"

Connected through hyperedge HE-3 (induced by source paper "an active information seeking model for goal oriented vision and language tasks"):
  with members:
    - "compositional attention networks for machine reasoning"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"
    - "inferring and executing programs for visual reasoning"
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "a simple neural network module for relational reasoning"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "stein variational policy gradient"

Connected through hyperedge HE-4 (induced by source paper "modeling friends and foes"):
  with members:
    - "the mechanics of n player differentiable games"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "ai safety gridworlds"
    - "the numerics of gans"
    - "concrete problems in ai safety"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "intriguing properties of neural networks"
    - "the best of both worlds stochastic and adversarial bandits"

Connected through hyperedge HE-5 (induced by source paper "systems of bounded rational agents with information theoretic constraints"):
  with members:
    - "a constraint handling technique for genetic algorithms using a violation factor"
    - "provably bounded optimal agents"

Connected through hyperedge HE-6 (induced by source paper "stein variational policy gradient"):
  with members:
    - "count based exploration with neural density models"
    - "reinforcement learning with deep energy based policies"
    - "deep exploration via bootstrapped dqn"
    - "sample efficient actor critic with experience replay"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "improving policy gradient by exploring under appreciated rewards"
    - "q prop sample efficient policy gradient with an off policy critic"

Connected through hyperedge HE-7 (induced by source paper "hierarchical state abstractions for decision making problems with computational constraints"):
  with members:
    - "planning with information processing constraints and model uncertainty in markov decision processes"

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"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a bayesian rule for adaptive control based on causal interventions"
    - "a minimum relative entropy principle for learning and acting"
    - "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)