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: "structureflow image inpainting via structure aware appearance flow"
  Abstract: Image inpainting techniques have shown significant improvements by using deep neural networks recently. However, most of them may either fail to reconstruct reasonable structures or restore fine-grained textures. In order to solve this problem, in this paper, we propose a two-stage model which splits the inpainting task into two parts: structure reconstruction and texture generation. In the first stage, edge-preserved smooth images are employed to train a structure reconstructor which completes the missing structures of the inputs. In the second stage, based on the reconstructed structures, a texture generator using appearance flow is designed to yield image details. Experiments on multiple publicly available datasets show the superior performance of the proposed network.

Connected through hyperedge HE-1 (induced by source paper "deep image spatial transformation for person image generation"):
  with members:
    - "multi channel attention selection gans for guided image to image translation"
    - "progressive pose attention transfer for person image generation"
    - "unsupervised person image generation with semantic parsing transformation"
    - "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"
    - "linearized multi sampling for differentiable image transformation"
    - "dense intrinsic appearance flow for human pose transfer"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "local relation networks for image recognition"

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"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "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 "guidance and evaluation semantic aware image inpainting for mixed scenes"):
  with members:
    - "learning pyramid context encoder network for high quality image inpainting"
    - "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-4 (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"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "selfie self supervised pretraining for image embedding"
    - "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-5 (induced by source paper "image fine grained inpainting"):
  with members:
    - "progressive perception oriented network for single image super resolution"
    - "foreground aware image inpainting"
    - "pluralistic image completion"
    - "non stationary texture synthesis by adversarial expansion"
    - "the relativistic discriminator a key element missing from standard gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"

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)