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: "safe mutations for deep and recurrent neural networks through output gradients"
  Abstract: While neuroevolution (evolving neural networks) has a successful track record across a variety of domains from reinforcement learning to artificial life, it is rarely applied to large, deep neural networks. A central reason is that while random mutation generally works in low dimensions, a random perturbation of thousands or millions of weights is likely to break existing functionality, providing no learning signal even if some individual weight changes were beneficial. This paper proposes a solution by introducing a family of safe mutation (SM) operators that aim within the mutation operator itself to find a degree of change that does not alter network behavior too much, but still facilitates exploration. Importantly, these SM operators do not require any additional interactions with the environment. The most effective SM variant capitalizes on the intriguing opportunity to scale the degree of mutation of each individual weight according to the sensitivity of the network's outputs to that weight, which requires computing the gradient of outputs with respect to the weights (instead of the gradient of error, as in conventional deep learning). This safe mutation through gradients (SM-G) operator dramatically increases the ability of a simple genetic algorithm-based neuroevolution method to find solutions in high-dimensional domains that require deep and/or recurrent neural networks (which tend to be particularly brittle to mutation), including domains that require processing raw pixels. By improving our ability to evolve deep neural networks, this new safer approach to mutation expands the scope of domains amenable to neuroevolution.

Connected through hyperedge HE-1 (induced by source paper "improving deep neuroevolution via deep innovation protection"):
  with members:
    - "weight agnostic neural networks"
    - "deep neuroevolution of recurrent and discrete world models"
    - "learning latent dynamics for planning from pixels"
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"
    - "unsupervised predictive memory in a goal directed agent"
    - "recurrent world models facilitate policy evolution"
    - "scalable co optimization of morphology and control in embodied machines"
    - "autoencoder augmented neuroevolution for visual doom playing"

Connected through hyperedge HE-2 (induced by source paper "evolving neural networks through a reverse encoding tree"):
  with members:
    - "behaviour suite for reinforcement learning"
    - "generalized binary search for split neighborly problems"
    - "vine an open source interactive data visualization tool for neuroevolution"
    - "anti jamming communications using spectrum waterfall a deep reinforcement learning approach"
    - "complex networks from simple rewrite systems"

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 "deep neuroevolution of recurrent and discrete world models"):
  with members:
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "unsupervised predictive memory in a goal directed agent"
    - "recurrent world models facilitate policy evolution"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "evolving deep neural networks"
    - "autoencoder augmented neuroevolution for visual doom playing"

Connected through hyperedge HE-5 (induced by source paper "neural architecture evolution in deep reinforcement learning for continuous control"):
  with members:
    - "proximal distilled evolutionary reinforcement learning"
    - "evolving rewards to automate reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "learning to design rna"
    - "addressing function approximation error in actor critic methods"
    - "evolving deep neural networks"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-6 (induced by source paper "monte carlo tree search for policy optimization"):
  with members:
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "evolution guided policy gradient in reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "proximal policy optimization algorithms"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-7 (induced by source paper "proximal distilled evolutionary reinforcement learning"):
  with members:
    - "collaborative evolutionary reinforcement learning"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "evolution guided policy gradient in reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "parameter space noise for exploration"
    - "hindsight experience replay"
    - "proximal policy optimization algorithms"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "guided evolutionary strategies escaping the curse of dimensionality in random search"):
  with members:
    - "structured evolution with compact architectures for scalable policy optimization"
    - "understanding short horizon bias in stochastic meta optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "world models"
    - "evolutionary stochastic gradient descent for optimization of deep neural networks"
    - "evolved policy gradients"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"

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)