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: "a linear time natural evolution strategy for non separable functions"
  Abstract: We present a novel Natural Evolution Strategy (NES) variant, the Rank-One NES (R1-NES), which uses a low rank approximation of the search distribution covariance matrix. The algorithm allows computation of the natural gradient with cost linear in the dimensionality of the parameter space, and excels in solving high-dimensional non-separable problems, including the best result to date on the Rosenbrock function (512 dimensions).

Connected through hyperedge HE-1 (induced by source paper "one big net for everything"):
  with members:
    - "progressive reinforcement learning with distillation for multi skilled motion control"
    - "progressive neural networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep learning in neural networks"
    - "on learning to think algorithmic information theory for novel combinations of reinforcement learning controllers and recurrent neural world models"
    - "show and tell a neural image caption generator"
    - "first experiments with powerplay"
    - "self delimiting neural networks"

Connected through hyperedge HE-2 (induced by source paper "challenges in high dimensional reinforcement learning with evolution strategies"):
  with members:
    - "parameter space noise for exploration"
    - "limited memory matrix adaptation for large scale black box optimization"
    - "es is more than just a traditional finite difference approximator"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "coco a platform for comparing continuous optimizers in a black box setting"
    - "a computationally efficient limited memory cma es for large scale optimization"
    - "evolutionary algorithms for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "limited memory matrix adaptation for large scale black box optimization"):
  with members:
    - "cma es for hyperparameter optimization of deep neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (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-5 (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"

Connected through hyperedge HE-6 (induced by source paper "lm cma an alternative to l bfgs for large scale black box optimization"):
  with members:
    - "maximum likelihood based online adaptation of hyper parameters in cma es"
    - "a computationally efficient limited memory cma es for large scale optimization"
    - "linear convergence of comparison based step size adaptive randomized search via stability of markov chains"
    - "objective improvement in information geometric optimization"
    - "self adaptive surrogate assisted covariance matrix adaptation evolution strategy"

Connected through hyperedge HE-7 (induced by source paper "a computationally efficient limited memory cma es for large scale optimization"):
  with members:
    - "self adaptive surrogate assisted covariance matrix adaptation evolution strategy"

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)