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: "gaussian processes for sample efficient reinforcement learning with rmax like exploration"
  Abstract: We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achieve low sample complexity. To achieve low sample complexity, since the environment is unknown, an agent must intelligently balance exploration and exploitation, and must be able to rapidly generalize from observations. While in the past a number of related sample efficient RL algorithms have been proposed, to allow theoretical analysis, mainly model-learners with weak generalization capabilities were considered. Here, we separate function approximation in the model learner (which does require samples) from the interpolation in the planner (which does not require samples). For model-learning we apply Gaussian processes regression (GP) which is able to automatically adjust itself to the complexity of the problem (via Bayesian hyperparameter selection) and, in practice, often able to learn a highly accurate model from very little data. In addition, a GP provides a natural way to determine the uncertainty of its predictions, which allows us to implement the "optimism in the face of uncertainty" principle used to efficiently control exploration. Our method is evaluated on four common benchmark domains.

Connected through hyperedge HE-1 (induced by source paper "output feedback online optimal control for a class of nonlinear systems"):
  with members:
    - "model based reinforcement learning in differential graphical games"
    - "model based reinforcement learning for infinite horizon approximate optimal tracking"
    - "simultaneous state and parameter estimation for second order nonlinear systems"
    - "efficient model based reinforcement learning for approximate online optimal control"
    - "model based reinforcement learning for approximate optimal regulation"
    - "learning continuous control policies by stochastic value gradients"
    - "approximate optimal trajectory tracking for continuous time nonlinear systems"

Connected through hyperedge HE-2 (induced by source paper "context dependent upper confidence bounds for directed exploration"):
  with members:
    - "parameter space noise for exploration"
    - "shallow updates for deep reinforcement learning"
    - "count based exploration with neural density models"
    - "count based exploration in feature space for reinforcement learning"
    - "bounded optimal exploration in mdp"
    - "deep exploration via bootstrapped dqn"
    - "bayesian optimal control of smoothly parameterized systems the lazy posterior sampling algorithm"
    - "dyna style planning with linear function approximation and prioritized sweeping"

Connected through hyperedge HE-3 (induced by source paper "online learning in kernelized markov decision processes"):
  with members:
    - "bayesian optimization under heavy tailed payoffs"
    - "learning unknown markov decision processes a thompson sampling approach"
    - "on kernelized multi armed bandits"
    - "safe exploration in finite markov decision processes with gaussian processes"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "online regret bounds for undiscounted continuous reinforcement learning"
    - "finite time analysis of kernelised contextual bandits"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

Connected through hyperedge HE-4 (induced by source paper "multi fidelity reinforcement learning with gaussian processes"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "sim to real learning agile locomotion for quadruped robots"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"

Connected through hyperedge HE-5 (induced by source paper "active learning for autonomous intelligent agents exploration curiosity and interaction"):
  with members:
    - "multi class generalized binary search for active inverse reinforcement learning"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "selecting the state representation in reinforcement learning"
    - "local utility elicitation in gai models"
    - "variance based rewards for approximate bayesian reinforcement learning"
    - "near optimal nonmyopic value of information in graphical models"
    - "safe exploration in markov decision processes"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-6 (induced by source paper "better optimism by bayes adaptive planning with rich models"):
  with members:
    - "model based bayesian reinforcement learning in large structured domains"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "a bayesian sampling approach to exploration in reinforcement learning"
    - "safe exploration in markov decision processes"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-7 (induced by source paper "optimized look ahead tree policies a bridge between look ahead tree policies and direct policy search"):
  with members:
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"
    - "evolutionary algorithms for reinforcement learning"

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)