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: "image inpainting using block wise procedural training with annealed adversarial counterpart"
  Abstract: Recent advances in deep generative models have shown promising potential in image inpanting, which refers to the task of predicting missing pixel values of an incomplete image using the known context. However, existing methods can be slow or generate unsatisfying results with easily detectable flaws. In addition, there is often perceivable discontinuity near the holes and require further post-processing to blend the results. We present a new approach to address the difficulty of training a very deep generative model to synthesize high-quality photo-realistic inpainting. Our model uses conditional generative adversarial networks (conditional GANs) as the backbone, and we introduce a novel block-wise procedural training scheme to stabilize the training while we increase the network depth. We also propose a new strategy called adversarial loss annealing to reduce the artifacts. We further describe several losses specifically designed for inpainting and show their effectiveness. Extensive experiments and user-study show that our approach outperforms existing methods in several tasks such as inpainting, face completion and image harmonization. Finally, we show our framework can be easily used as a tool for interactive guided inpainting, demonstrating its practical value to solve common real-world challenges.

Connected through hyperedge HE-1 (induced by source paper "harmonizing maximum likelihood with gans for multimodal conditional generation"):
  with members:
    - "on the convergence of adam and beyond"
    - "video to video synthesis"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "video prediction with appearance and motion conditions"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "free form image inpainting with gated convolution"

Connected through hyperedge HE-2 (induced by source paper "coherent semantic attention for image inpainting"):
  with members:
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "the relativistic discriminator a key element missing from standard gan"
    - "free form image inpainting with gated convolution"
    - "shift net image inpainting via deep feature rearrangement"
    - "guided image inpainting replacing an image region by pulling content from another image"
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "contextual based image inpainting infer match and translate"

Connected through hyperedge HE-3 (induced by source paper "smart deep copy paste"):
  with members:
    - "faceshop deep sketch based face image editing"
    - "a style based generator architecture for generative adversarial networks"
    - "guided image inpainting replacing an image region by pulling content from another image"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"
    - "deep bilateral learning for real time image enhancement"
    - "real time user guided image colorization with learned deep priors"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "free form image inpainting with gated convolution"):
  with members:
    - "deep video inpainting"
    - "pepsi fast and lightweight network for image inpainting"
    - "foreground aware image inpainting"
    - "pluralistic image completion"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "image inpainting for irregular holes using partial convolutions"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "spg net segmentation prediction and guidance network for image inpainting"):
  with members:
    - "image inpainting for irregular holes using partial convolutions"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"
    - "deep image prior"
    - "contextual based image inpainting infer match and translate"
    - "improved training of wasserstein gans"

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)