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: "cem rl combining evolutionary and gradient based methods for policy search"
  Abstract: Deep neuroevolution and deep reinforcement learning (deep RL) algorithms are two popular approaches to policy search. The former is widely applicable and rather stable, but suffers from low sample efficiency. By contrast, the latter is more sample efficient, but the most sample efficient variants are also rather unstable and highly sensitive to hyper-parameter setting. So far, these families of methods have mostly been compared as competing tools. However, an emerging approach consists in combining them so as to get the best of both worlds. Two previously existing combinations use either an ad hoc evolutionary algorithm or a goal exploration process together with the Deep Deterministic Policy Gradient (DDPG) algorithm, a sample efficient off-policy deep RL algorithm. In this paper, we propose a different combination scheme using the simple cross-entropy method (CEM) and Twin Delayed Deep Deterministic policy gradient (td3), another off-policy deep RL algorithm which improves over ddpg. We evaluate the resulting method, cem-rl, on a set of benchmarks classically used in deep RL. We show that cem-rl benefits from several advantages over its competitors and offers a satisfactory trade-off between performance and sample efficiency.

Connected through hyperedge HE-1 (induced by source paper "effective diversity in population based reinforcement learning"):
  with members:
    - "novelty search for deep reinforcement learning policy network weights by action sequence edit metric distance"
    - "collaborative evolutionary reinforcement learning"
    - "provably robust blackbox optimization for reinforcement learning"
    - "behavior guided reinforcement learning"
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "adapting behaviour for learning progress"
    - "attraction repulsion actor critic for continuous control reinforcement learning"
    - "a generalized framework for population based training"

Connected through hyperedge HE-2 (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"
    - "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"
    - "policy search in continuous action domains an overview"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"

Connected through hyperedge HE-3 (induced by source paper "population guided parallel policy search for reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "scalable coordinated exploration in concurrent reinforcement learning"
    - "self imitation learning"
    - "coordinated exploration in concurrent reinforcement learning"
    - "evolution guided policy gradient in reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "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"
    - "policy search in continuous action domains an overview"

Connected through hyperedge HE-5 (induced by source paper "learning graph convolutional network for skeleton based human action recognition by neural searching"):
  with members:
    - "video action recognition via neural architecture searching"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "darts differentiable architecture search"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"

Connected through hyperedge HE-6 (induced by source paper "fidi rl incorporating deep reinforcement learning with finite difference policy search for efficient learning of continuous control"):
  with members:
    - "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"
    - "parameter space noise for exploration"

Connected through hyperedge HE-7 (induced by source paper "q learning for continuous actions with cross entropy guided policies"):
  with members:
    - "pre training with non expert human demonstration for deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "composable deep reinforcement learning for robotic manipulation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "maximum a posteriori policy optimisation"

Connected through hyperedge HE-8 (induced by source paper "attraction repulsion actor critic for continuous control reinforcement learning"):
  with members:
    - "collaborative evolutionary reinforcement learning"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "randomized value functions via multiplicative normalizing flows"
    - "evolution guided policy gradient in reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "boosting trust region policy optimization by normalizing flows policy"
    - "bayesian policy gradients via alpha divergence dropout inference"

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)