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 "fcsr gan joint face completion and super resolution via multi task learning"):
  Abstract: Combined variations containing low-resolution and occlusion often present in face images in the wild, e.g., under the scenario of video surveillance. While most of the existing face image recovery approaches can handle only one type of variation per model, in this work, we propose a deep generative adversarial network (FCSR-GAN) for performing joint face completion and face super-resolution via multi-task learning. The generator of FCSR-GAN aims to recover a high-resolution face image without occlusion given an input low-resolution face image with occlusion. The discriminator of FCSR-GAN uses a set of carefully designed losses (an adversarial loss, a perceptual loss, a pixel loss, a smooth loss, a style loss, and a face prior loss) to assure the high quality of the recovered high-resolution face images without occlusion. The whole network of FCSR-GAN can be trained end-to-end using our two-stage training strategy. Experimental results on the public-domain CelebA and Helen databases show that the proposed approach outperforms the state-of-the-art methods in jointly performing face super-resolution (up to 8 $\times$) and face completion, and shows good generalization ability in cross-database testing. Our FCSR-GAN is also useful for improving face identification performance when there are low-resolution and occlusion in face images.

Includes members:
  Member "feedback network for image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "feedback graph convolutional network for skeleton based action recognition")
  Member "learning pyramid context encoder network for high quality image inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "grounded and controllable image completion by incorporating lexical semantics")
  Member "coherent semantic attention for image inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "guidance and evaluation semantic aware image inpainting for mixed scenes")
  Member "pluralistic image completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval")
  Member "adaptive transform domain image super resolution via orthogonally regularized deep networks":
    also connected through other hyperedges:
      (none)
  Member "geometry aware face completion and editing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint face completion and super resolution using multi scale feature relation learning")
  Member "tattoo image search at scale joint detection and compact representation learning":
    also connected through other hyperedges:
      (none)
  Member "generative image inpainting with contextual attention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)