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: "edgeconnect generative image inpainting with adversarial edge learning"
  Abstract: Over the last few years, deep learning techniques have yielded significant improvements in image inpainting. However, many of these techniques fail to reconstruct reasonable structures as they are commonly over-smoothed and/or blurry. This paper develops a new approach for image inpainting that does a better job of reproducing filled regions exhibiting fine details. We propose a two-stage adversarial model EdgeConnect that comprises of an edge generator followed by an image completion network. The edge generator hallucinates edges of the missing region (both regular and irregular) of the image, and the image completion network fills in the missing regions using hallucinated edges as a priori. We evaluate our model end-to-end over the publicly available datasets CelebA, Places2, and Paris StreetView, and show that it outperforms current state-of-the-art techniques quantitatively and qualitatively. Code and models available at: this https URL

Connected through hyperedge HE-1 (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"
    - "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"
    - "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-2 (induced by source paper "piigan generative adversarial networks for pluralistic image inpainting"):
  with members:
    - "learning pyramid context encoder network for high quality image inpainting"
    - "coherent semantic attention for image inpainting"
    - "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-3 (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"
    - "structureflow image inpainting via structure aware appearance flow"
    - "unsupervised object segmentation by redrawing"
    - "noise2void learning denoising from single noisy images"
    - "seigan towards compositional image generation by simultaneously learning to segment enhance and inpaint"

Connected through hyperedge HE-4 (induced by source paper "vcnet a robust approach to blind image inpainting"):
  with members:
    - "image inpainting with learnable bidirectional attention maps"
    - "foreground aware image inpainting"
    - "pluralistic image completion"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "a style based generator architecture for generative adversarial networks"
    - "the contextual loss for image transformation with non aligned data"
    - "image inpainting via generative multi column convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "defoggan predicting hidden information in the starcraft fog of war with generative adversarial nets"):
  with members:
    - "single image haze removal using conditional wasserstein generative adversarial networks"
    - "macro action selection with deep reinforcement learning in starcraft"
    - "forward modeling for partial observation strategy games a starcraft defogger"
    - "clear the fog combat value assessment in incomplete information games with convolutional encoder decoders"
    - "learning macromanagement in starcraft from replays using deep learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "improved training of wasserstein gans"
    - "stardata a starcraft ai research dataset"

Connected through hyperedge HE-6 (induced by source paper "learning to incorporate structure knowledge for image inpainting"):
  with members:
    - "learning pyramid context encoder network for high quality image inpainting"
    - "foreground aware image inpainting"
    - "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-7 (induced by source paper "large hole image inpainting with compress decompression network"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "generative image inpainting with contextual attention"
    - "context encoders feature learning by inpainting"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "syn2real forgery classification via unsupervised domain adaptation"):
  with members:
    - "deep visual domain adaptation a survey"
    - "deep image matting"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "deep domain confusion maximizing for domain invariance"

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)