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: "automatically designing cnn architectures using genetic algorithm for image classification"
  Abstract: Convolutional Neural Networks (CNNs) have gained a remarkable success on many real-world problems in recent years. However, the performance of CNNs is highly relied on their architectures. For some state-of-the-art CNNs, their architectures are hand-crafted with expertise in both CNNs and the investigated problems. To this end, it is difficult for researchers, who have no extended expertise in CNNs, to explore CNNs for their own problems of interest. In this paper, we propose an automatic architecture design method for CNNs by using genetic algorithms, which is capable of discovering a promising architecture of a CNN on handling image classification tasks. The proposed algorithm does not need any pre-processing before it works, nor any post-processing on the discovered CNN, which means it is completely automatic. The proposed algorithm is validated on widely used benchmark datasets, by comparing to the state-of-the-art peer competitors covering eight manually designed CNNs, four semi-automatically designed CNNs and additional four automatically designed CNNs. The experimental results indicate that the proposed algorithm achieves the best classification accuracy consistently among manually and automatically designed CNNs. Furthermore, the proposed algorithm also shows the competitive classification accuracy to the semi-automatic peer competitors, while reducing 10 times of the parameters. In addition, on the average the proposed algorithm takes only one percentage of computational resource compared to that of all the other architecture discovering algorithms.

Connected through hyperedge HE-1 (induced by source paper "arctext an unified text approach to describing convolutional neural network architectures"):
  with members:
    - "peephole predicting network performance before training"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "pairnets novel fast shallow artificial neural networks on partitioned subspaces"):
  with members:
    - "optimization of convolutional neural network using microcanonical annealing algorithm"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "a new compensatory genetic algorithm based method for effective compressed multi function convolutional neural network model selection with multi objective optimization"):
  with members:
    - "nsga net a multi objective genetic algorithm for neural architecture search"
    - "optimization of convolutional neural network using microcanonical annealing algorithm"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "pruning convolutional neural networks for resource efficient inference"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "evolutionary dataset optimisation learning algorithm quality through evolution"):
  with members:
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "understanding and diagnosing visual tracking systems"

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)