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 hybrid differential evolution approach to designing deep convolutional neural networks for image classification"
  Abstract: Convolutional Neural Networks (CNNs) have demonstrated their superiority in image classification, and evolutionary computation (EC) methods have recently been surging to automatically design the architectures of CNNs to save the tedious work of manually designing CNNs. In this paper, a new hybrid differential evolution (DE) algorithm with a newly added crossover operator is proposed to evolve the architectures of CNNs of any lengths, which is named DECNN. There are three new ideas in the proposed DECNN method. Firstly, an existing effective encoding scheme is refined to cater for variable-length CNN architectures; Secondly, the new mutation and crossover operators are developed for variable-length DE to optimise the hyperparameters of CNNs; Finally, the new second crossover is introduced to evolve the depth of the CNN architectures. The proposed algorithm is tested on six widely-used benchmark datasets and the results are compared to 12 state-of-the-art methods, which shows the proposed method is vigorously competitive to the state-of-the-art algorithms. Furthermore, the proposed method is also compared with a method using particle swarm optimisation with a similar encoding strategy named IPPSO, and the proposed DECNN outperforms IPPSO in terms of the accuracy.

Connected through hyperedge HE-1 (induced by source paper "particle swarm optimisation for evolving deep neural networks for image classification by evolving and stacking transferable blocks"):
  with members:
    - "evolving deep neural networks by multi objective particle swarm optimization for image classification"
    - "a hybrid ga pso method for evolving architecture and short connections of deep convolutional neural networks"
    - "regularized evolution for image classifier architecture search"
    - "evolving deep convolutional neural networks by variable length particle swarm optimization for image classification"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"
    - "efficient architecture search by network transformation"

Connected through hyperedge HE-2 (induced by source paper "a hybrid ga pso method for evolving architecture and short connections of deep convolutional neural networks"):
  with members:
    - "automatically designing cnn architectures using genetic algorithm for image classification"
    - "evolving deep convolutional neural networks by variable length particle swarm optimization for image classification"
    - "skip connections eliminate singularities"
    - "learning transferable architectures for scalable image recognition"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "evolving deep convolutional neural networks for image classification"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "evolving deep neural networks by multi objective particle swarm optimization for image classification"):
  with members:
    - "evolving deep convolutional neural networks by variable length particle swarm optimization for image classification"
    - "large scale evolution of image classifiers"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "condensenet an efficient densenet using learned group convolutions"
    - "densely connected convolutional networks"
    - "polynet a pursuit of structural diversity in very deep networks"
    - "neural architecture search with 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)