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: "efficient natural evolution strategies"
  Abstract: Efficient Natural Evolution Strategies (eNES) is a novel alternative to conventional evolutionary algorithms, using the natural gradient to adapt the mutation distribution. Unlike previous methods based on natural gradients, eNES uses a fast algorithm to calculate the inverse of the exact Fisher information matrix, thus increasing both robustness and performance of its evolution gradient estimation, even in higher dimensions. Additional novel aspects of eNES include optimal fitness baselines and importance mixing (a procedure for updating the population with very few fitness evaluations). The algorithm yields competitive results on both unimodal and multimodal benchmarks.

Connected through hyperedge HE-1 (induced by source paper "limitations of the empirical fisher approximation for natural gradient descent"):
  with members:
    - "information matrices and generalization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "slang fast structured covariance approximations for bayesian deep learning with natural gradient"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "three factors influencing minima in sgd"
    - "signsgd compressed optimisation for non convex problems"
    - "noisy natural gradient as variational inference"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"

Connected through hyperedge HE-2 (induced by source paper "improving evolutionary strategies with generative neural networks"):
  with members:
    - "neural generative models for global optimization with gradients"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "effective evaluation using logged bandit feedback from multiple loggers"
    - "proximal policy optimization algorithms"
    - "density estimation using real nvp"
    - "cma es for hyperparameter optimization of deep neural networks"
    - "benchmarking deep reinforcement learning for continuous control"
    - "the cma evolution strategy a tutorial"

Connected through hyperedge HE-3 (induced by source paper "diagonal acceleration for covariance matrix adaptation evolution strategies"):
  with members:
    - "lm cma an alternative to l bfgs for large scale black box optimization"
    - "distributed bayesian learning with stochastic natural gradient expectation propagation and the posterior server"
    - "alternative restart strategies for cma es"

Connected through hyperedge HE-4 (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-5 (induced by source paper "deep learning in neural networks"):
  with members:
    - "going deeper with convolutions"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "a clockwork rnn"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "sequence to sequence learning with neural networks"
    - "multi column deep neural networks for offline handwritten chinese character classification"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "efficient visual coding from retina to v2"

Connected through hyperedge HE-6 (induced by source paper "on learning to think algorithmic information theory for novel combinations of reinforcement learning controllers and recurrent neural world models"):
  with members:
    - "deep learning in neural networks"
    - "going deeper with convolutions"
    - "neural turing machines"
    - "deep speech scaling up end to end speech recognition"
    - "a clockwork rnn"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"
    - "visualizing and understanding convolutional networks"

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.AI(Artificial Intelligence)