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 "psgan a generative adversarial network for remote sensing image pan sharpening"):
  Abstract: Remote sensing image fusion (also known as pan-sharpening) aims to generate a high resolution multi-spectral image from inputs of a high spatial resolution single band panchromatic (PAN) image and a low spatial resolution multi-spectral (MS) image. In this paper, we propose PSGAN, a generative adversarial network (GAN) for remote sensing image pan-sharpening. To the best of our knowledge, this is the first attempt at producing high quality pan-sharpened images with GANs. The PSGAN consists of two parts. Firstly, a two-stream fusion architecture is designed to generate the desired high resolution multi-spectral images, then a fully convolutional network serving as a discriminator is applied to distinct "real" or "pan-sharpened" MS images. Experiments on images acquired by Quickbird and GaoFen-1 satellites demonstrate that the proposed PSGAN can fuse PAN and MS images effectively and significantly improve the results over the state of the art traditional and CNN based pan-sharpening methods.

Includes members:
  Member "learning continuous face age progression a pyramid of gans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stylegan2 distillation for feed forward image manipulation")
  Member "target adaptive cnn based pansharpening":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "s3 a spectral spatial structure loss for pan sharpening networks")
  Member "boosting the accuracy of multispectral image pansharpening by learning a deep residual network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised and unregistered hyperspectral image super resolution with mutual dirichlet net")
  Member "remote sensing image fusion based on two stream fusion network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "from w net to cdgan bitemporal change detection via deep learning techniques")
  Member "multi resolution data fusion for super resolution electron microscopy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "accelerating the super resolution convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "photo realistic single image super resolution using a generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "image to image translation with conditional adversarial networks":
    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)