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: "planning with information processing constraints and model uncertainty in markov decision processes"
  Abstract: Information-theoretic principles for learning and acting have been proposed to solve particular classes of Markov Decision Problems. Mathematically, such approaches are governed by a variational free energy principle and allow solving MDP planning problems with information-processing constraints expressed in terms of a Kullback-Leibler divergence with respect to a reference distribution. Here we consider a generalization of such MDP planners by taking model uncertainty into account. As model uncertainty can also be formalized as an information-processing constraint, we can derive a unified solution from a single generalized variational principle. We provide a generalized value iteration scheme together with a convergence proof. As limit cases, this generalized scheme includes standard value iteration with a known model, Bayesian MDP planning, and robust planning. We demonstrate the benefits of this approach in a grid world simulation.

Connected through hyperedge HE-1 (induced by source paper "a unified bellman optimality principle combining reward maximization and empowerment"):
  with members:
    - "mutual information regularization in markov decision processes and actor critic learning"
    - "empowerment driven exploration using mutual information estimation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "a unified strategy for implementing curiosity and empowerment driven reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-2 (induced by source paper "robust reinforcement learning for continuous control with model misspecification"):
  with members:
    - "challenges of real world reinforcement learning"
    - "a bayesian approach to robust reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "soft actor critic algorithms and applications"
    - "unicorn continual learning with a universal off policy agent"

Connected through hyperedge HE-3 (induced by source paper "mutual information regularization in markov decision processes and actor critic learning"):
  with members:
    - "an information theoretic on line learning principle for specialization in hierarchical decision making systems"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "equivalence between policy gradients and soft q learning"
    - "an information theoretic optimality principle for deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "disentangled skill embeddings for reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "data efficient hierarchical reinforcement learning"
    - "multi task deep reinforcement learning with popart"
    - "meta reinforcement learning of structured exploration strategies"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "decoupling dynamics and reward for transfer learning"
    - "maximum a posteriori policy optimisation"

Connected through hyperedge HE-5 (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"
    - "an information theoretic on line update principle for perception action coupling"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "bounded rational decision making with adaptive neural network priors"):
  with members:
    - "an information theoretic on line update principle for perception action coupling"
    - "regularised deep reinforcement learning with guaranteed convergence"
    - "divide and conquer reinforcement learning"
    - "neural adaptive sequential monte carlo"
    - "reversible jump mcmc simulated annealing for neural networks"

Connected through hyperedge HE-7 (induced by source paper "balancing two player stochastic games with soft q learning"):
  with members:
    - "regularised deep reinforcement learning with guaranteed convergence"
    - "an information theoretic optimality principle for deep reinforcement learning"
    - "trust region policy optimization"
    - "toward large scale agent guidance in an urban taxi service"

Connected through hyperedge HE-8 (induced by source paper "ai safety gridworlds"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "adversarial attacks on neural network policies"
    - "interactive learning from policy dependent human feedback"
    - "low impact artificial intelligences"
    - "towards proving the adversarial robustness of deep neural networks"
    - "a distributional perspective on reinforcement learning"
    - "enter the matrix safely interruptible autonomous systems via virtualization"
    - "should robots be obedient"

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)