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: "metaheuristic algorithms for convolution neural network"
  Abstract: A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).

Connected through hyperedge HE-1 (induced by source paper "optimization of convolutional neural network using the linearly decreasing weight particle swarm optimization"):
  with members:
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"):
  with members:
    - "bin ct urban waste collection based on predicting the container fill level"
    - "low cost recurrent neural network expected performance evaluation"
    - "evolutionary architecture search for deep multitask networks"
    - "dlopt deep learning optimization library"
    - "evolving deep neural networks"
    - "a survey on deep learning in medical image analysis"
    - "metaheuristic design of feedforward neural networks"
    - "neural networks designing neural networks multi objective hyper parameter optimization"

Connected through hyperedge HE-3 (induced by source paper "evolutionary construction of convolutional neural networks"):
  with members:
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "evolution in groups a deeper look at synaptic cluster driven evolution of deep neural networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "evolving parsimonious networks by mixing activation functions"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "large scale evolution of convolutional neural networks using volunteer computing"

Connected through hyperedge HE-4 (induced by source paper "optimization of convolutional neural network using microcanonical annealing algorithm"):
  with members:
    - "all you need is a good init"
    - "going deeper with convolutions"
    - "striving for simplicity the all convolutional net"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "matconvnet convolutional neural networks for matlab"
    - "fractional max pooling"

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.CV(Computer Vision and Pattern Recognition)