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: "es is more than just a traditional finite difference approximator"
  Abstract: An evolution strategy (ES) variant recently attracted significant attention due to its surprisingly good performance at optimizing neural networks in challenging deep reinforcement learning domains. It searches directly in the parameter space of neural networks by generating perturbations to the current set of parameters, checking their performance, and moving in the direction of higher reward. The resemblance of this algorithm to a traditional finite-difference approximation of the reward gradient in parameter space naturally leads to the assumption that it is just that. However, this assumption is incorrect. The aim of this paper is to definitively demonstrate this point empirically. ES is a gradient approximator, but optimizes for a different gradient than just reward (especially when the magnitude of candidate perturbations is high). Instead, it optimizes for the average reward of the entire population, often also promoting parameters that are robust to perturbation. This difference can channel ES into significantly different areas of the search space than gradient descent in parameter space, and also consequently to networks with significantly different properties. This unique robustness-seeking property, and its consequences for optimization, are demonstrated in several domains. They include humanoid locomotion, where networks from policy gradient-based reinforcement learning are far less robust to parameter perturbation than ES-based policies that solve the same task. While the implications of such robustness and robustness-seeking remain open to further study, the main contribution of this work is to highlight that such differences indeed exist and deserve attention.

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 "collaborative evolutionary reinforcement learning"):
  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"
    - "deep reinforcement learning that matters"
    - "parameter space noise for exploration"
    - "hindsight experience replay"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"

Connected through hyperedge HE-3 (induced by source paper "evolutionary computation and ai safety research problems impeding routine and safe real world application of evolution"):
  with members:
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "agi safety literature review"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "supervising strong learners by amplifying weak experts"
    - "a study on overfitting in deep reinforcement learning"
    - "scalable agent alignment via reward modeling a research direction"
    - "quantifying generalization in reinforcement learning"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"

Connected through hyperedge HE-4 (induced by source paper "surreal system fully integrated stack for distributed deep reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "tvm end to end optimization stack for deep learning"
    - "distributed prioritized experience replay"
    - "vta an open hardware software stack for deep learning"
    - "deep reinforcement learning that matters"
    - "ray a distributed framework for emerging ai applications"
    - "rllib abstractions for distributed reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "non differentiable supervised learning with evolution strategies and hybrid methods"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "the state of sparsity in deep neural networks"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "regularized evolution for image classifier architecture search"
    - "efficient neural audio synthesis"
    - "guided evolutionary strategies escaping the curse of dimensionality in random search"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-6 (induced by source paper "acoustic model optimization based on evolutionary stochastic gradient descent with anchors for automatic speech recognition"):
  with members:
    - "evolutionary stochastic gradient descent for optimization of deep neural networks"
    - "population based training of neural networks"
    - "the cma evolution strategy a tutorial"
    - "adam a method for stochastic optimization"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-7 (induced by source paper "network of evolvable neural units evolving to learn at a synaptic level"):
  with members:
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "learning to learn by gradient descent by gradient descent"
    - "asynchronous methods for deep reinforcement learning"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-8 (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"

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.NE(Neural and Evolutionary Computing)