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 "facial information recovery from heavily damaged images using generative adversarial network part 1"):
  Abstract: Over the past decades, a large number of techniques have emerged in modern imaging systems to capture the exact information of the original scene regardless of shake, motion, lighting conditions and etc., These developments have progressively addressed the acquisition of images in high speed and high resolutions. However, the various ineradicable real-time factors cause the degradation of the information and the quality of the acquired images. The available techniques are not intelligent enough to generalize this complex phenomenon. Hence, it is necessary to develop an intellectual framework to recover the possible information presented in the original scene. In this article, we propose a kernel free framework based on conditional-GAN to recover the information from the heavily damaged images. The degradation of images is assumed to be occurred by the combination of a various blur. Learning parameter of the cGAN is optimized by multi-component loss function that includes improved wasserstein loss with regression loss function. The generator module of this network is developed by using U-Net architecture with local Residual connections and global skip connection. Local connections and a global skip connection are implemented for the utilization of all stages of features. Generated images show that the network has the potential to recover the probable information of blurred images from the learned features. This research work is carried out as a part of our IOP studio software 'Facial recognition module'.

Includes members:
  Member "implementation of deep convolutional neural network in multi class categorical image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning the sequential temporal information with recurrent neural networks")
  Member "feed forward and backward run in deep convolution neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "implementation of deep convolutional neural network in multi class categorical image classification")
  Member "deep generative filter for motion deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "motion deblurring of faces")
  Member "regularization and optimization strategies in deep convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "interactive subspace exploration on generative image modelling")
  Member "photo realistic single image super resolution using a generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "improved techniques for training gans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "semantic image inpainting with deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)