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: "regularization and optimization strategies in deep convolutional neural network"
  Abstract: Convolution Neural Networks, known as ConvNets exceptionally perform well in many complex machine learning tasks. The architecture of ConvNets demands the huge and rich amount of data and involves with a vast number of parameters that leads the learning takes to be computationally expensive, slow convergence towards the global minima, trap in local minima with poor predictions. In some cases, architecture overfits the data and make the architecture difficult to generalise for new samples that were not in the training set samples. To address these limitations, many regularization and optimization strategies are developed for the past few years. Also, studies suggested that these techniques significantly increase the performance of the networks as well as reducing the computational cost. In implementing these techniques, one must thoroughly understand the theoretical concept of how this technique works in increasing the expressive power of the networks. This article is intended to provide the theoretical concepts and mathematical formulation of the most commonly used strategies in developing a ConvNet architecture.

Connected through hyperedge HE-1 (induced by source paper "learning optimal data augmentation policies via bayesian optimization for image classification tasks"):
  with members:
    - "style transfer through back translation"
    - "autoaugment learning augmentation policies from data"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "improved regularization of convolutional neural networks with cutout"
    - "shake shake regularization"
    - "a bayesian data augmentation approach for learning deep models"

Connected through hyperedge HE-2 (induced by source paper "n fold superposition improving neural networks by reducing the noise in feature maps"):
  with members:
    - "l2 constrained softmax loss for discriminative face verification"
    - "squeeze and excitation networks"
    - "can deep neural networks match the related objects a survey on imagenet trained classification models"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "norm based capacity control in neural networks"

Connected through hyperedge HE-3 (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"
    - "feed forward and backward run in deep convolution neural network"
    - "deep generative filter for motion deblurring"
    - "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-4 (induced by source paper "implementation of deep convolutional neural network in multi class categorical image classification"):
  with members:
    - "feed forward and backward run in deep convolution neural network"
    - "early stopping without a validation set"
    - "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-5 (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"
    - "feed forward and backward run in deep convolution neural network"
    - "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior"

Connected through hyperedge HE-6 (induced by source paper "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior"):
  with members:
    - "feed forward and backward run in deep convolution 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)