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: "path integral policy improvement with covariance matrix adaptation"
  Abstract: There has been a recent focus in reinforcement learning on addressing continuous state and action problems by optimizing parameterized policies. PI2 is a recent example of this approach. It combines a derivation from first principles of stochastic optimal control with tools from statistical estimation theory. In this paper, we consider PI2 as a member of the wider family of methods which share the concept of probability-weighted averaging to iteratively update parameters to optimize a cost function. We compare PI2 to other members of the same family - Cross-Entropy Methods and CMAES - at the conceptual level and in terms of performance. The comparison suggests the derivation of a novel algorithm which we call PI2-CMA for "Path Integral Policy Improvement with Covariance Matrix Adaptation". PI2-CMA's main advantage is that it determines the magnitude of the exploration noise automatically.

Connected through hyperedge HE-1 (induced by source paper "the differentiable cross entropy method"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "exploring model based planning with policy networks"
    - "the limited multi label projection layer"
    - "bayesian optimization in variational latent spaces with dynamic compression"
    - "unsupervised state representation learning in atari"
    - "adaptive and safe bayesian optimization in high dimensions via one dimensional subspaces"
    - "learning latent plans from play"
    - "learning action representations for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "fidi rl incorporating deep reinforcement learning with finite difference policy search for efficient learning of continuous control"):
  with members:
    - "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"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"
    - "deep reinforcement learning that matters"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-3 (induced by source paper "prescribed performance control guided policy improvement for satisfying signal temporal logic tasks"):
  with members:
    - "a policy search method for temporal logic specified reinforcement learning tasks"
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "robust control for signal temporal logic specifications using average space robustness"
    - "path integral guided policy search"
    - "robust temporal logic model predictive control"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"

Connected through hyperedge HE-4 (induced by source paper "a view of estimation of distribution algorithms through the lens of expectation maximization"):
  with members:
    - "conditioning by adaptive sampling for robust design"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "new insights and perspectives on the natural gradient method"
    - "fast variational inference in the conjugate exponential family"

Connected through hyperedge HE-5 (induced by source paper "policy search in continuous action domains an overview"):
  with members:
    - "hierarchical reinforcement learning with hindsight"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "data efficient hierarchical reinforcement learning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "first order and second order variants of the gradient descent a unified framework"
    - "state representation learning for control an overview"

Connected through hyperedge HE-6 (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"
    - "policy search in continuous action domains an overview"
    - "importance mixing improving sample reuse in evolutionary policy search methods"
    - "guided evolutionary strategies escaping the curse of dimensionality in random search"
    - "evolved policy gradients"

Connected through hyperedge HE-7 (induced by source paper "importance mixing improving sample reuse in evolutionary policy search methods"):
  with members:
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "policy search in continuous action domains an overview"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "proximal policy optimization algorithms"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "mirror descent search and its acceleration"):
  with members:
    - "proximal policy optimization algorithms"
    - "guided policy search as approximate mirror descent"
    - "provable bayesian inference via particle mirror descent"
    - "trust region policy optimization"
    - "sparse q learning with mirror descent"

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)