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: "contextual based image inpainting infer match and translate"
  Abstract: We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to generate visually coherent completion given a high-resolution image with missing components. In order to overcome the difficulty to directly learn the distribution of high-dimensional image data, we divide the task into inference and translation as two separate steps and model each step with a deep neural network. We also use simple heuristics to guide the propagation of local textures from the boundary to the hole. We show that, by using such techniques, inpainting reduces to the problem of learning two image-feature translation functions in much smaller space and hence easier to train. We evaluate our method on several public datasets and show that we generate results of better visual quality than previous state-of-the-art methods.

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"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "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"

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"
    - "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"

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"
    - "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"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "image fine grained inpainting"):
  with members:
    - "progressive perception oriented network for single image super resolution"
    - "structureflow image inpainting via structure aware appearance flow"
    - "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"

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"
    - "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"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "region wise generative adversarial imageinpainting for large missing areas"):
  with members:
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "pluralistic image completion"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "image inpainting for irregular holes using partial convolutions"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "an empirical study on evaluation metrics of generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "edgeconnect generative image inpainting with adversarial edge learning"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "richer convolutional features for edge detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "eye in painting with exemplar 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)