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 "stylegan2 distillation for feed forward image manipulation"):
  Abstract: StyleGAN2 is a state-of-the-art network in generating realistic images. Besides, it was explicitly trained to have disentangled directions in latent space, which allows efficient image manipulation by varying latent factors. Editing existing images requires embedding a given image into the latent space of StyleGAN2. Latent code optimization via backpropagation is commonly used for qualitative embedding of real world images, although it is prohibitively slow for many applications. We propose a way to distill a particular image manipulation of StyleGAN2 into image-to-image network trained in paired way. The resulting pipeline is an alternative to existing GANs, trained on unpaired data. We provide results of human faces' transformation: gender swap, aging/rejuvenation, style transfer and image morphing. We show that the quality of generation using our method is comparable to StyleGAN2 backpropagation and current state-of-the-art methods in these particular tasks.

Includes members:
  Member "style generator inversion for image enhancement and animation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analyzing and improving the image quality of stylegan")
  Member "image2stylegan how to embed images into the stylegan latent space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep automodulators")
  Member "analyzing and improving the image quality of stylegan":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "learning continuous face age progression a pyramid of gans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ifan image instance full alignment networks for adaptive object detection")
  Member "face to parameter translation for game character auto creation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards high fidelity 3d face reconstruction from in the wild images using graph convolutional networks")
  Member "meta sim learning to generate synthetic datasets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural data server a large scale search engine for transfer learning data")
  Member "stargan v2 diverse image synthesis for multiple domains":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "freeze discriminator a simple baseline for fine tuning gans")
  Member "classification accuracy score for conditional generative models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

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)