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: "on the relationship between the openai evolution strategy and stochastic gradient descent"
  Abstract: Because stochastic gradient descent (SGD) has shown promise optimizing neural networks with millions of parameters and few if any alternatives are known to exist, it has moved to the heart of leading approaches to reinforcement learning (RL). For that reason, the recent result from OpenAI showing that a particular kind of evolution strategy (ES) can rival the performance of SGD-based deep RL methods with large neural networks provoked surprise. This result is difficult to interpret in part because of the lingering ambiguity on how ES actually relates to SGD. The aim of this paper is to significantly reduce this ambiguity through a series of MNIST-based experiments designed to uncover their relationship. As a simple supervised problem without domain noise (unlike in most RL), MNIST makes it possible (1) to measure the correlation between gradients computed by ES and SGD and (2) then to develop an SGD-based proxy that accurately predicts the performance of different ES population sizes. These innovations give a new level of insight into the real capabilities of ES, and lead also to some unconventional means for applying ES to supervised problems that shed further light on its differences from SGD. Incorporating these lessons, the paper concludes by demonstrating that ES can achieve 99% accuracy on MNIST, a number higher than any previously published result for any evolutionary method. While not by any means suggesting that ES should substitute for SGD in supervised learning, the suite of experiments herein enables more informed decisions on the application of ES within RL and other paradigms.

Connected through hyperedge HE-1 (induced by source paper "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"):
  with members:
    - "reinforcement learning for improving agent design"
    - "unsupervised meta learning for reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"
    - "evolving multimodal robot behavior via many stepping stones with the combinatorial multi objective evolutionary algorithm"
    - "distributed prioritized experience replay"
    - "diversity is all you need learning skills without a reward function"
    - "episodic curiosity through reachability"

Connected through hyperedge HE-2 (induced by source paper "evolution attack on neural networks"):
  with members:
    - "generative adversarial trainer defense to adversarial perturbations with gan"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "adversarial examples for semantic segmentation and object detection"
    - "mask r cnn"
    - "practical black box attacks against machine learning"
    - "adversarial diversity and hard positive generation"
    - "universal adversarial perturbations"
    - "the cma evolution strategy a tutorial"

Connected through hyperedge HE-3 (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-4 (induced by source paper "stabilizing off policy reinforcement learning with conservative policy gradients"):
  with members:
    - "distributional policy optimization an alternative approach for continuous control"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "tight regret bounds for model based reinforcement learning with greedy policies"
    - "addressing function approximation error in actor critic methods"
    - "policy certificates towards accountable reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "safe policy improvement with baseline bootstrapping"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-5 (induced by source paper "creative ai through evolutionary computation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "rainbow combining improvements in deep reinforcement learning"
    - "discovering evolutionary stepping stones through behavior domination"
    - "deep learning in neural networks"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (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-7 (induced by source paper "evolutionary stochastic gradient descent for optimization of deep neural networks"):
  with members:
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"
    - "evolving deep neural networks"
    - "progressive neural architecture search"
    - "gradient free policy architecture search and adaptation"
    - "es is more than just a traditional finite difference approximator"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-8 (induced by source paper "data efficient neuroevolution with kernel based surrogate models"):
  with members:
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "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"
    - "data efficient exploration optimization and modeling of diverse designs through surrogate assisted illumination"
    - "black box data efficient policy search for robotics"
    - "robots that can adapt like animals"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

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)