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: "guided evolutionary strategies escaping the curse of dimensionality in random search"
  Abstract: Many applications in machine learning require optimizing a function whose true gradient is unknown, but where surrogate gradient information (directions that may be correlated with, but not necessarily identical to, the true gradient) is available instead. This arises when an approximate gradient is easier to compute than the full gradient (e.g. in meta-learning or unrolled optimization), or when a true gradient is intractable and is replaced with a surrogate (e.g. in certain reinforcement learning applications, or when using synthetic gradients). We propose Guided Evolutionary Strategies, a method for optimally using surrogate gradient directions along with random search. We define a search distribution for evolutionary strategies that is elongated along a guiding subspace spanned by the surrogate gradients. This allows us to estimate a descent direction which can then be passed to a first-order optimizer. We analytically and numerically characterize the tradeoffs that result from tuning how strongly the search distribution is stretched along the guiding subspace, and we use this to derive a setting of the hyperparameters that works well across problems. Finally, we apply our method to example problems including truncated unrolled optimization and a synthetic gradient problem, demonstrating improvement over both standard evolutionary strategies and first-order methods that directly follow the surrogate gradient. We provide a demo of Guided ES at this https URL

Connected through hyperedge HE-1 (induced by source paper "the differentiable cross entropy method"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "exploring model based planning with policy networks"
    - "the limited multi label projection layer"
    - "bayesian optimization in variational latent spaces with dynamic compression"
    - "unsupervised state representation learning in atari"
    - "adaptive and safe bayesian optimization in high dimensions via one dimensional subspaces"
    - "learning latent plans from play"
    - "learning action representations for reinforcement learning"

Connected through hyperedge HE-2 (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"
    - "don t decay the learning rate increase the batch size"
    - "deep rewiring training very sparse deep networks"

Connected through hyperedge HE-3 (induced by source paper "learning to learn by zeroth order oracle"):
  with members:
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "learning to optimize neural nets"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"
    - "query efficient hard label black box attack an optimization based approach"
    - "learning gradient descent better generalization and longer horizons"
    - "learned optimizers that scale and generalize"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning driven heuristic optimization"):
  with members:
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "evolution guided policy gradient in reinforcement learning"
    - "device placement optimization with reinforcement learning"
    - "proximal policy optimization algorithms"
    - "neural architecture search with reinforcement learning"
    - "evolutionary algorithms for reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "2019 evolutionary algorithms review"):
  with members:
    - "evolving simple programs for playing atari games"
    - "markov brains a technical introduction"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "gene expression programming a new adaptive algorithm for solving problems"

Connected through hyperedge HE-6 (induced by source paper "evolutionary computation optimization and learning algorithms for data science"):
  with members:
    - "isea image steganalysis using evolutionary algorithms"
    - "network representation learning a survey"
    - "feature engineering for predictive modeling using reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "cem rl combining evolutionary and gradient based methods for policy search"):
  with members:
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "addressing function approximation error in actor critic methods"
    - "policy search in continuous action domains an overview"
    - "importance mixing improving sample reuse in evolutionary policy search methods"
    - "evolved policy gradients"
    - "deep reinforcement learning that matters"

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)