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 convolutional neural networks by variable length particle swarm optimization for image classification"
  Abstract: Convolutional neural networks (CNNs) are one of the most effective deep learning methods to solve image classification problems, but the best architecture of a CNN to solve a specific problem can be extremely complicated and hard to design. This paper focuses on utilising Particle Swarm Optimisation (PSO) to automatically search for the optimal architecture of CNNs without any manual work involved. In order to achieve the goal, three improvements are made based on traditional PSO. First, a novel encoding strategy inspired by computer networks which empowers particle vectors to easily encode CNN layers is proposed; Second, in order to allow the proposed method to learn variable-length CNN architectures, a Disabled layer is designed to hide some dimensions of the particle vector to achieve variable-length particles; Third, since the learning process on large data is slow, partial datasets are randomly picked for the evaluation to dramatically speed it up. The proposed algorithm is examined and compared with 12 existing algorithms including the state-of-art methods on three widely used image classification benchmark datasets. The experimental results show that the proposed algorithm is a strong competitor to the state-of-art algorithms in terms of classification error. This is the first work using PSO for automatically evolving the architectures of CNNs.

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"
    - "a hybrid differential evolution approach to designing deep convolutional neural networks for image classification"
    - "regularized evolution for image classifier architecture search"
    - "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:
    - "a hybrid differential evolution approach to designing deep convolutional neural networks for image classification"
    - "automatically designing cnn architectures using genetic algorithm 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:
    - "a hybrid differential evolution approach to designing deep convolutional neural networks 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"

Connected through hyperedge HE-4 (induced by source paper "a hybrid differential evolution approach to designing deep convolutional neural networks for image classification"):
  with members:
    - "evolving unsupervised deep neural networks for learning meaningful representations"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "pcanet a simple deep learning baseline for image classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "convolutional neural networks for sentence classification"
    - "invariant scattering convolution networks"
    - "learning invariant representations with local transformations"

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)