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: "non stationary texture synthesis by adversarial expansion"
  Abstract: The real world exhibits an abundance of non-stationary textures. Examples include textures with large-scale structures, as well as spatially variant and inhomogeneous textures. While existing example-based texture synthesis methods can cope well with stationary textures, non-stationary textures still pose a considerable challenge, which remains unresolved. In this paper, we propose a new approach for example-based non-stationary texture synthesis. Our approach uses a generative adversarial network (GAN), trained to double the spatial extent of texture blocks extracted from a specific texture exemplar. Once trained, the fully convolutional generator is able to expand the size of the entire exemplar, as well as of any of its sub-blocks. We demonstrate that this conceptually simple approach is highly effective for capturing large-scale structures, as well as other non-stationary attributes of the input exemplar. As a result, it can cope with challenging textures, which, to our knowledge, no other existing method can handle.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "on demand solid texture synthesis using deep 3d networks"):
  with members:
    - "texture mixer a network for controllable synthesis and interpolation of texture"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "deep image prior"
    - "diversified texture synthesis with feed forward networks"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "learning texture manifolds with the periodic spatial gan"
    - "universal style transfer via feature transforms"

Connected through hyperedge HE-3 (induced by source paper "gan leaks a taxonomy of membership inference attacks against gans"):
  with members:
    - "texture mixer a network for controllable synthesis and interpolation of texture"
    - "exploiting unintended feature leakage in collaborative learning"
    - "attributing fake images to gans learning and analyzing gan fingerprints"
    - "privacy protective gan for face de identification"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "differentially private generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "sean image synthesis with semantic region adaptive normalization"):
  with members:
    - "image2stylegan how to embed images into the stylegan latent space"
    - "controllable artistic text style transfer via shape matching gan"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "mask guided portrait editing with conditional gans"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-5 (induced by source paper "singan learning a generative model from a single natural image"):
  with members:
    - "surprising effectiveness of few image unsupervised feature learning"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "ingan capturing and remapping the dna of a natural image"
    - "the perception distortion tradeoff"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "deep painterly harmonization"

Connected through hyperedge HE-6 (induced by source paper "controllable and progressive image extrapolation"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "foreground aware image inpainting"
    - "semantic image synthesis with spatially adaptive normalization"
    - "very long natural scenery image prediction by outpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "linknet relational embedding for scene graph"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"

Connected through hyperedge HE-7 (induced by source paper "texture mixer a network for controllable synthesis and interpolation of texture"):
  with members:
    - "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"
    - "progressive growing of gans for improved quality stability and variation"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"

Connected through hyperedge HE-8 (induced by source paper "user controllable multi texture synthesis with generative adversarial networks"):
  with members:
    - "reconstruction of 3d porous media from 2d slices"
    - "improving generalization and stability of generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "texturegan controlling deep image synthesis with texture patches"
    - "diversified texture synthesis with feed forward networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "reconstruction of three dimensional porous media using generative adversarial neural 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.GR(Graphics)