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: "evolution guided policy gradient in reinforcement learning"
  Abstract: Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically suffer from three core difficulties: temporal credit assignment with sparse rewards, lack of effective exploration, and brittle convergence properties that are extremely sensitive to hyperparameters. Collectively, these challenges severely limit the applicability of these approaches to real-world problems. Evolutionary Algorithms (EAs), a class of black box optimization techniques inspired by natural evolution, are well suited to address each of these three challenges. However, EAs typically suffer from high sample complexity and struggle to solve problems that require optimization of a large number of parameters. In this paper, we introduce Evolutionary Reinforcement Learning (ERL), a hybrid algorithm that leverages the population of an EA to provide diversified data to train an RL agent, and reinserts the RL agent into the EA population periodically to inject gradient information into the EA. ERL inherits EA's ability of temporal credit assignment with a fitness metric, effective exploration with a diverse set of policies, and stability of a population-based approach and complements it with off-policy DRL's ability to leverage gradients for higher sample efficiency and faster learning. Experiments in a range of challenging continuous control benchmarks demonstrate that ERL significantly outperforms prior DRL and EA methods.

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 "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"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "scalable coordinated exploration in concurrent reinforcement learning"
    - "self imitation learning"
    - "coordinated exploration in concurrent reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "periodic intra ensemble knowledge distillation for reinforcement learning"):
  with members:
    - "population guided parallel policy search for reinforcement learning"
    - "information asymmetry in kl regularized rl"
    - "knowledge distillation by on the fly native ensemble"
    - "off policy deep reinforcement learning without exploration"
    - "soft actor critic algorithms and applications"
    - "learning to walk via deep reinforcement learning"
    - "deep mutual learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-4 (induced by source paper "a survey of reinforcement learning techniques strategies recent development and future directions"):
  with members:
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "hindsight experience replay"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "proximal policy optimization algorithms"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "collaborative evolutionary reinforcement learning"):
  with members:
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "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 that matters"
    - "parameter space noise for exploration"
    - "hindsight experience replay"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "count based exploration with neural density models"

Connected through hyperedge HE-7 (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"
    - "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-8 (induced by source paper "evolutionary reinforcement learning for sample efficient multiagent coordination"):
  with members:
    - "collaborative evolutionary reinforcement learning"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "emergent coordination through competition"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "addressing function approximation error in actor critic methods"
    - "pommerman a multi agent playground"
    - "counterfactual multi agent policy gradients"
    - "a multi agent reinforcement learning model of common pool resource appropriation"

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)