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: "genetic algorithms for evolving deep neural networks"
  Abstract: In recent years, deep learning methods applying unsupervised learning to train deep layers of neural networks have achieved remarkable results in numerous fields. In the past, many genetic algorithms based methods have been successfully applied to training neural networks. In this paper, we extend previous work and propose a GA-assisted method for deep learning. Our experimental results indicate that this GA-assisted approach improves the performance of a deep autoencoder, producing a sparser neural network.

Connected through hyperedge HE-1 (induced by source paper "automatic design of artificial neural networks for gamma ray detection"):
  with members:
    - "evolutionary neural automl for deep learning"
    - "denser deep evolutionary network structured representation"
    - "fast denser evolving fully trained deep artificial neural networks"
    - "evolving deep neural networks"
    - "towards the evolution of multi layered neural networks a dynamic structured grammatical evolution approach"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "gradient descent based optimization algorithms for deep learning models training"):
  with members:
    - "segen sample ensemble genetic evolutionary network model"
    - "gadam genetic evolutionary adam for deep neural network optimization"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "adam a method for stochastic optimization"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "gacnn training deep convolutional neural networks with genetic algorithm"):
  with members:
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "gadam genetic evolutionary adam for deep neural network optimization"):
  with members:
    - "segen sample ensemble genetic evolutionary network model"
    - "deep forest towards an alternative to deep neural networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "improving generalization performance by switching from adam to sgd"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "texture classification in extreme scale variations using ganet"):
  with members:
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"
    - "describing textures in the wild"

Connected through hyperedge HE-6 (induced by source paper "negatively correlated search"):
  with members:
    - "on the compilability and expressive power of propositional planning formalisms"

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)