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: "an information theoretic on line update principle for perception action coupling"
  Abstract: Inspired by findings of sensorimotor coupling in humans and animals, there has recently been a growing interest in the interaction between action and perception in robotic systems [Bogh et al., 2016]. Here we consider perception and action as two serial information channels with limited information-processing capacity. We follow [Genewein et al., 2015] and formulate a constrained optimization problem that maximizes utility under limited information-processing capacity in the two channels. As a solution we obtain an optimal perceptual channel and an optimal action channel that are coupled such that perceptual information is optimized with respect to downstream processing in the action module. The main novelty of this study is that we propose an online optimization procedure to find bounded-optimal perception and action channels in parameterized serial perception-action systems. In particular, we implement the perceptual channel as a multi-layer neural network and the action channel as a multinomial distribution. We illustrate our method in a NAO robot simulator with a simplified cup lifting task.

Connected through hyperedge HE-1 (induced by source paper "an information theoretic on line learning principle for specialization in hierarchical decision making systems"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "systems of bounded rational agents with information theoretic constraints"
    - "bounded rational decision making with adaptive neural network priors"
    - "divide and conquer reinforcement learning"
    - "constrained policy optimization"
    - "an information theoretic optimality principle for deep reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "planning with information processing constraints and model uncertainty in markov decision processes"

Connected through hyperedge HE-2 (induced by source paper "bounded rational decision making with adaptive neural network priors"):
  with members:
    - "regularised deep reinforcement learning with guaranteed convergence"
    - "divide and conquer reinforcement learning"
    - "planning with information processing constraints and model uncertainty in markov decision processes"
    - "neural adaptive sequential monte carlo"
    - "reversible jump mcmc simulated annealing for neural networks"

Connected through hyperedge HE-3 (induced by source paper "regularised deep reinforcement learning with guaranteed convergence"):
  with members:
    - "decentralised learning in systems with many many strategic agents"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "balancing two player stochastic games with soft q learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "equivalence between policy gradients and soft q learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "proximal policy optimization algorithms"

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)