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 "cdgan cyclic discriminative generative adversarial networks for image to image transformation"):
  Abstract: Image-to-image transformation is a kind of problem, where the input image from one visual representation is transformed into the output image of another visual representation. Since 2014, Generative Adversarial Networks (GANs) have facilitated a new direction to tackle this problem by introducing the generator and the discriminator networks in its architecture. Many recent works, like Pix2Pix, CycleGAN, DualGAN, PS2MAN and CSGAN handled this problem with the required generator and discriminator networks and choice of the different losses that are used in the objective functions. In spite of these works, still there is a gap to fill in terms of both the quality of the images generated that should look more realistic and as much as close to the ground truth images. In this work, we introduce a new Image-to-Image Transformation network named Cyclic Discriminative Generative Adversarial Networks (CDGAN) that fills the above mentioned gaps. The proposed CDGAN generates high quality and more realistic images by incorporating the additional discriminator networks for cycled images in addition to the original architecture of the CycleGAN. To demonstrate the performance of the proposed CDGAN, it is tested over three different baseline image-to-image transformation datasets. The quantitative metrics such as pixel-wise similarity, structural level similarity and perceptual level similarity are used to judge the performance. Moreover, the qualitative results are also analyzed and compared with the state-of-the-art methods. The proposed CDGAN method clearly outperformed all the state-of-the-art methods when compared over the three baseline Image-to-Image transformation datasets.

Includes members:
  Member "csgan cyclic synthesized generative adversarial networks for image to image transformation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cali sketch stroke calibration and completion for high quality face image generation from poorly drawn sketches")
  Member "generative adversarial networks and perceptual losses for video super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep blind video super resolution")
  Member "recsys dan discriminative adversarial networks for cross domain recommender systems":
    also connected through other hyperedges:
      (none)
  Member "the unreasonable effectiveness of deep features as a perceptual metric":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "perceptual adversarial networks for image to image transformation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "medgan medical image translation using gans")
  Member "high quality facial photo sketch synthesis using multi adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "dualgan unsupervised dual learning for image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)