human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "implementation of deep convolutional neural network in multi class categorical image classification"):
  Abstract: Convolutional Neural Networks has been implemented in many complex machine learning takes such as image classification, object identification, autonomous vehicle and robotic vision tasks. However, ConvNet architecture efficiency and accuracy depend on a large number of fac- tors. Also, the complex architecture requires a significant amount of data to train and involves with a large number of hyperparameters that increases the computational expenses and difficul- ties. Hence, it is necessary to address the limitations and techniques to overcome the barriers to ensure that the architecture performs well in complex visual tasks. This article is intended to develop an efficient ConvNet architecture for multi-class image categorical classification applica- tion. In the development of the architecture, large pool of grey scale images are taken as input information images and split into training and test datasets. The numerously available technique is implemented to reduce the overfitting and poor generalization of the network. The hyperpa- rameters of determined by Bayesian Optimization with Gaussian Process prior algorithm. ReLu non-linear activation function is implemented after the convolutional layers. Max pooling op- eration is carried out to downsampling the data points in pooling layers. Cross-entropy loss function is used to measure the performance of the architecture where the softmax is used in the classification layer. Mini-batch gradient descent with Adam optimizer algorithm is used for backpropagation. Developed architecture is validated with confusion matrix and classification report.

Includes members:
  Member "feed forward and backward run in deep convolution neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "facial information recovery from heavily damaged images using generative adversarial network part 1")
  Member "early stopping without a validation set":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the efficacy of knowledge distillation")
  Member "regularization and optimization strategies in deep convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning optimal data augmentation policies via bayesian optimization for image classification tasks")
  Member "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interactive subspace exploration on generative image modelling")
  Member "deep networks with stochastic depth":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on feature normalization and data augmentation")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "auto weka combined selection and hyperparameter optimization of classification algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats")
  Member "improving neural networks by preventing co adaptation of feature detectors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hyper parameter optimization a review of algorithms and applications")

Question: Please tell me which class the source paper 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)