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: "policy search in continuous action domains an overview"
  Abstract: Continuous action policy search, the search for efficient policies in continuous control tasks, is currently the focus of intensive research driven both by the recent success of deep reinforcement learning algorithms and by the emergence of competitors based on evolutionary algorithms. In this paper, we present a broad survey of policy search methods, incorporating into a common big picture these very different approaches as well as alternatives such as Bayesian Optimization and directed exploration methods. The main message of this overview is in the relationship between the families of methods, but we also outline some factors underlying sample efficiency properties of the various approaches. Besides, to keep this survey as short and didactic as possible, we do not go into the details of mathematical derivations of the elementary algorithms.

Connected through hyperedge HE-1 (induced by source paper "accelerating reinforcement learning with a directional gaussian smoothing evolution strategy"):
  with members:
    - "provably robust blackbox optimization for reinforcement learning"
    - "when random search is not enough sample efficient and noise robust blackbox optimization of rl policies"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "addressing function approximation error in actor critic methods"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"

Connected through hyperedge HE-2 (induced by source paper "language as an abstraction for hierarchical deep reinforcement learning"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "a survey of reinforcement learning informed by natural language"
    - "hierarchical reinforcement learning with hindsight"
    - "temporal difference models model free deep rl for model based control"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-3 (induced by source paper "multi path policy optimization"):
  with members:
    - "collaborative evolutionary reinforcement learning"
    - "diversity inducing policy gradient using maximum mean discrepancy to find a set of diverse policies"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "path consistency learning in tsallis entropy regularized mdps"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-4 (induced by source paper "coride joint order dispatching and fleet management for multi scale ride hailing platforms"):
  with members:
    - "cityflow a multi agent reinforcement learning environment for large scale city traffic scenario"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "colight learning network level cooperation for traffic signal control"
    - "data efficient hierarchical reinforcement learning"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "learning multi level hierarchies with hindsight"
    - "meta learning shared hierarchies"

Connected through hyperedge HE-5 (induced by source paper "hierarchical reinforcement learning for concurrent discovery of compound and composable policies"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "soft actor critic algorithms and applications"
    - "the intentional unintentional agent learning to solve many continuous control tasks simultaneously"
    - "a unified view of entropy regularized markov decision processes"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "continuous control with deep reinforcement learning"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-6 (induced by source paper "data efficient hierarchical reinforcement learning"):
  with members:
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "distributed distributional deterministic policy gradients"
    - "meta learning shared hierarchies"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "automatic goal generation for reinforcement learning agents"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "cem rl combining evolutionary and gradient based methods for policy search"):
  with members:
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "addressing function approximation error in actor critic methods"
    - "importance mixing improving sample reuse in evolutionary policy search methods"
    - "guided evolutionary strategies escaping the curse of dimensionality in random search"
    - "evolved policy gradients"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-8 (induced by source paper "ppo cma proximal policy optimization with covariance matrix adaptation"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "unity a general platform for intelligent agents"
    - "soft actor critic algorithms and applications"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "intelligent middle level game control"
    - "maximum a posteriori policy optimisation"
    - "a survey on policy search algorithms for learning robot controllers in a handful of trials"
    - "relative entropy regularized policy iteration"

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.LG(Machine Learning)