human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "learning pyramid context encoder network for high quality image inpainting"
  Abstract: High-quality image inpainting requires filling missing regions in a damaged image with plausible content. Existing works either fill the regions by copying image patches or generating semantically-coherent patches from region context, while neglect the fact that both visual and semantic plausibility are highly-demanded. In this paper, we propose a Pyramid-context ENcoder Network (PEN-Net) for image inpainting by deep generative models. The PEN-Net is built upon a U-Net structure, which can restore an image by encoding contextual semantics from full resolution input, and decoding the learned semantic features back into images. Specifically, we propose a pyramid-context encoder, which progressively learns region affinity by attention from a high-level semantic feature map and transfers the learned attention to the previous low-level feature map. As the missing content can be filled by attention transfer from deep to shallow in a pyramid fashion, both visual and semantic coherence for image inpainting can be ensured. We further propose a multi-scale decoder with deeply-supervised pyramid losses and an adversarial loss. Such a design not only results in fast convergence in training, but more realistic results in testing. Extensive experiments on various datasets show the superior performance of the proposed network

Connected through hyperedge HE-1 (induced by source paper "grounded and controllable image completion by incorporating lexical semantics"):
  with members:
    - "universal dependency parsing from scratch"
    - "foreground aware image inpainting"
    - "pluralistic image completion"
    - "unconstrained foreground object search"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "mc gan multi conditional generative adversarial network for image synthesis"
    - "hole filling with multiple reference views in dibr view synthesis"

Connected through hyperedge HE-2 (induced by source paper "domain embedded multi model generative adversarial networks for image based face inpainting"):
  with members:
    - "image inpainting with learnable bidirectional attention maps"
    - "deep fusion network for image completion"
    - "structureflow image inpainting via structure aware appearance flow"
    - "foreground aware image inpainting"
    - "pluralistic image completion"
    - "alphagan generative adversarial networks for natural image matting"
    - "semantic image inpainting through improved wasserstein generative adversarial networks"
    - "face completion with semantic knowledge and collaborative adversarial learning"

Connected through hyperedge HE-3 (induced by source paper "joint face completion and super resolution using multi scale feature relation learning"):
  with members:
    - "facial expression restoration based on improved graph convolutional networks"
    - "fcsr gan joint face completion and super resolution via multi task learning"
    - "geometry aware face completion and editing"
    - "graph neural networks a review of methods and applications"
    - "rain streak removal for single image via kernel guided cnn"
    - "learning a discriminative prior for blind image deblurring"
    - "cycle dehaze enhanced cyclegan for single image dehazing"
    - "generative face completion"

Connected through hyperedge HE-4 (induced by source paper "guidance and evaluation semantic aware image inpainting for mixed scenes"):
  with members:
    - "structureflow image inpainting via structure aware appearance flow"
    - "foreground aware image inpainting"
    - "coherent semantic attention for image inpainting"
    - "pluralistic image completion"
    - "semantic image synthesis with spatially adaptive normalization"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "spg net segmentation prediction and guidance network for image inpainting"

Connected through hyperedge HE-5 (induced by source paper "piigan generative adversarial networks for pluralistic image inpainting"):
  with members:
    - "coherent semantic attention for image inpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "shift net image inpainting via deep feature rearrangement"
    - "image inpainting via generative multi column convolutional neural networks"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-6 (induced by source paper "instance separation emerges from inpainting"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "learning correspondence from the cycle consistency of time"
    - "selfie self supervised pretraining for image embedding"
    - "structureflow image inpainting via structure aware appearance flow"
    - "unsupervised object segmentation by redrawing"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "noise2void learning denoising from single noisy images"
    - "seigan towards compositional image generation by simultaneously learning to segment enhance and inpaint"

Connected through hyperedge HE-7 (induced by source paper "learning to incorporate structure knowledge for image inpainting"):
  with members:
    - "foreground aware image inpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "free form image inpainting with gated convolution"
    - "image inpainting via generative multi column convolutional neural networks"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "contextual based image inpainting infer match and translate"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "image inpainting a review"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "a novel approach for robust multi human action detection and recognition based on 3 dimentional convolutional neural networks"
    - "blind visual motif removal from a single image"
    - "adversarial inpainting of medical image modalities"
    - "deep hyperspectral prior denoising inpainting super resolution"
    - "pepsi fast and lightweight network for image inpainting"
    - "foreground aware image inpainting"
    - "coherent semantic attention for image inpainting"

Question: Please tell me which class the center node 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)