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: "evolving deep neural networks"
  Abstract: Abstract   The success of deep learning depends on finding an architecture to fit the task. As deep learning has scaled up to more challenging tasks, the architectures have become difficult to design by hand. This paper proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution. By extending existing neuroevolution methods to topology, components, and hyperparameters, this method achieves results comparable to best human designs in standard benchmarks in object recognition and language modeling. It also supports building a real-world application of automated image captioning on a magazine website. Given the anticipated increases in available computing power, evolution of deep networks is promising approach to constructing deep learning applications in the future.

Connected through hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "towards modular and programmable architecture search"
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "nas count counting by density with neural architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-3 (induced by source paper "stabilizing differentiable architecture search via perturbation based regularization"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "understanding architectures learnt by cell based neural architecture search"
    - "efficient neural architecture search via proximal iterations"
    - "certified adversarial robustness via randomized smoothing"
    - "searching for a robust neural architecture in four gpu hours"
    - "fair darts eliminating unfair advantages in differentiable architecture search"
    - "understanding and robustifying differentiable architecture search"
    - "bayesnas a bayesian approach for neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "how to train your super net an analysis of training heuristics in weight sharing nas"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "nas bench 201 extending the scope of reproducible neural architecture search"
    - "random search and reproducibility for neural architecture search"
    - "on network design spaces for visual recognition"
    - "nas evaluation is frustratingly hard"
    - "searching for a robust neural architecture in four gpu hours"
    - "detnas backbone search for object detection"
    - "understanding and robustifying differentiable architecture search"

Connected through hyperedge HE-5 (induced by source paper "accelerating reinforcement learning with a directional gaussian smoothing evolution strategy"):
  with members:
    - "provably robust blackbox optimization for reinforcement learning"
    - "when random search is not enough sample efficient and noise robust blackbox optimization of rl policies"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "addressing function approximation error in actor critic methods"
    - "policy search in continuous action domains an overview"

Connected through hyperedge HE-6 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "frage frequency agnostic word representation"
    - "retina u net embarrassingly simple exploitation of segmentation supervision for medical object detection"
    - "automated soft tissue lesion detection and segmentation in digital mammography using a u net deep learning network"
    - "fast neural architecture search of compact semantic segmentation models via auxiliary cells"
    - "darts differentiable architecture search"
    - "deformable convnets v2 more deformable better results"

Connected through hyperedge HE-7 (induced by source paper "population based training for loss function optimization"):
  with members:
    - "evolving loss functions with multivariate taylor polynomial parameterizations"
    - "visualising basins of attraction for the cross entropy and the squared error neural network loss functions"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "improved training speed accuracy and data utilization through loss function optimization"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "nsga net neural architecture search using multi objective genetic algorithm"

Connected through hyperedge HE-8 (induced by source paper "automating representation discovery with map elites"):
  with members:
    - "covariance matrix adaptation for the rapid illumination of behavior space"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "discovering the elite hypervolume by leveraging interspecies correlation"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "deep interactive evolution"
    - "isolating sources of disentanglement in variational autoencoders"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for 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)