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: "feed forward and backward run in deep convolution neural network"
  Abstract: Convolution Neural Networks (CNN), known as ConvNets are widely used in many visual imagery application, object classification, speech recognition. After the implementation and demonstration of the deep convolution neural network in Imagenet classification in 2012 by krizhevsky, the architecture of deep Convolution Neural Network is attracted many researchers. This has led to the major development in Deep learning frameworks such as Tensorflow, caffe, keras, theno. Though the implementation of deep learning is quite possible by employing deep learning frameworks, mathematical theory and concepts are harder to understand for new learners and practitioners. This article is intended to provide an overview of ConvNets architecture and to explain the mathematical theory behind it including activation function, loss function, feedforward and backward propagation. In this article, grey scale image is taken as input information image, ReLU and Sigmoid activation function are considered for developing the architecture and cross-entropy loss function are used for computing the difference between predicted value and actual value. The architecture is developed in such a way that it can contain one convolution layer, one pooling layer, and multiple dense layers

Connected through hyperedge HE-1 (induced by source paper "facial information recovery from heavily damaged images using generative adversarial network part 1"):
  with members:
    - "implementation of deep convolutional neural network in multi class categorical image classification"
    - "deep generative filter for motion deblurring"
    - "regularization and optimization strategies in deep convolutional neural network"
    - "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"
    - "semantic image inpainting with deep generative models"
    - "semantic image inpainting with perceptual and contextual losses"

Connected through hyperedge HE-2 (induced by source paper "implementation of deep convolutional neural network in multi class categorical image classification"):
  with members:
    - "early stopping without a validation set"
    - "regularization and optimization strategies in deep convolutional neural network"
    - "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior"
    - "deep networks with stochastic depth"
    - "deep residual learning for image recognition"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learning the sequential temporal information with recurrent neural networks"):
  with members:
    - "implementation of deep convolutional neural network in multi class categorical image classification"
    - "regularization and optimization strategies in deep convolutional neural network"
    - "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior"

Connected through hyperedge HE-4 (induced by source paper "regularization and optimization strategies in deep convolutional neural network"):
  with members:
    - "early stopping without a validation set"
    - "deep networks with stochastic depth"
    - "deep residual learning for image recognition"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-5 (induced by source paper "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior"):
  with members:
    - "regularization and optimization strategies in deep convolutional neural network"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical 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.CV(Computer Vision and Pattern Recognition)