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 "joint face completion and super resolution using multi scale feature relation learning"):
  Abstract: Previous research on face restoration often focused on repairing a specific type of low-quality facial images such as low-resolution (LR) or occluded facial images. However, in the real world, both the above-mentioned forms of image degradation often coexist. Therefore, it is important to design a model that can repair LR occluded images simultaneously. This paper proposes a multi-scale feature graph generative adversarial network (MFG-GAN) to implement the face restoration of images in which both degradation modes coexist, and also to repair images with a single type of degradation. Based on the GAN, the MFG-GAN integrates the graph convolution and feature pyramid network to restore occluded low-resolution face images to non-occluded high-resolution face images. The MFG-GAN uses a set of customized losses to ensure that high-quality images are generated. In addition, we designed the network in an end-to-end format. Experimental results on the public-domain CelebA and Helen databases show that the proposed approach outperforms state-of-the-art methods in performing face super-resolution (up to 4x or 8x) and face completion simultaneously. Cross-database testing also revealed that the proposed approach has good generalizability.

Includes members:
  Member "facial expression restoration based on improved graph convolutional networks":
    also connected through other hyperedges:
      (none)
  Member "fcsr gan joint face completion and super resolution via multi task learning":
    also connected through other hyperedges:
      (none)
  Member "learning pyramid context encoder network for high quality image inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "grounded and controllable image completion by incorporating lexical semantics")
  Member "geometry aware face completion and editing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "everybody s talkin let me talk as you want")
  Member "graph neural networks a review of methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "rain streak removal for single image via kernel guided cnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "facial expression restoration based on improved graph convolutional networks")
  Member "learning a discriminative prior for blind image deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deblurgan v2 deblurring orders of magnitude faster and better")
  Member "cycle dehaze enhanced cyclegan for single image dehazing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deraincyclegan an attention guided unsupervised benchmark for single image deraining and rainmaking")

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)