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 "image inpainting with learnable bidirectional attention maps"):
  Abstract: Most convolutional network (CNN)-based inpainting methods adopt standard convolution to indistinguishably treat valid pixels and holes, making them limited in handling irregular holes and more likely to generate inpainting results with color discrepancy and blurriness. Partial convolution has been suggested to address this issue, but it adopts handcrafted feature re-normalization, and only considers forward mask-updating. In this paper, we present a learnable attention map module for learning feature renormalization and mask-updating in an end-to-end manner, which is effective in adapting to irregular holes and propagation of convolution layers. Furthermore, learnable reverse attention maps are introduced to allow the decoder of U-Net to concentrate on filling in irregular holes instead of reconstructing both holes and known regions, resulting in our learnable bidirectional attention maps. Qualitative and quantitative experiments show that our method performs favorably against state-of-the-arts in generating sharper, more coherent and visually plausible inpainting results. The source code and pre-trained models will be available.

Includes members:
  Member "foreground aware image inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fine grained image to image transformation towards visual recognition")
  Member "pluralistic image completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval")
  Member "edgeconnect generative image inpainting with adversarial edge learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "guidance and evaluation semantic aware image inpainting for mixed scenes")
  Member "free form image inpainting with gated convolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "shift net image inpainting via deep feature rearrangement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cascade ef gan progressive facial expression editing with local focuses")
  Member "image inpainting via generative multi column convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "generative image inpainting with contextual attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "contextual based image inpainting infer match and translate":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "grounded and controllable image completion by incorporating lexical semantics")

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)