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: "texture synthesis with spatial generative adversarial networks"
  Abstract: Generative adversarial networks (GANs) are a recent approach to train generative models of data, which have been shown to work particularly well on image data. In the current paper we introduce a new model for texture synthesis based on GAN learning. By extending the input noise distribution space from a single vector to a whole spatial tensor, we create an architecture with properties well suited to the task of texture synthesis, which we call spatial GAN (SGAN). To our knowledge, this is the first successful completely data-driven texture synthesis method based on GANs. #R##N#Our method has the following features which make it a state of the art algorithm for texture synthesis: high image quality of the generated textures, very high scalability w.r.t. the output texture size, fast real-time forward generation, the ability to fuse multiple diverse source images in complex textures. To illustrate these capabilities we present multiple experiments with different classes of texture images and use cases. We also discuss some limitations of our method with respect to the types of texture images it can synthesize, and compare it to other neural techniques for texture generation.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy"):
  with members:
    - "a layer based sequential framework for scene generation with gans"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "quick and easy time series generation with established image based gans"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep semantic hashing with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "intelligent home 3d automatic 3d house design from linguistic descriptions only"):
  with members:
    - "multi marginal wasserstein gan"
    - "object driven text to image synthesis via adversarial training"
    - "collaborative unsupervised domain adaptation for medical image diagnosis"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "semantics disentangling for text to image generation"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "text2scene generating compositional scenes from textual descriptions"
    - "to create what you tell generating videos from captions"

Connected through hyperedge HE-3 (induced by source paper "structured gans"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "learning texture manifolds with the periodic spatial gan"
    - "fader networks manipulating images by sliding attributes"
    - "perceptual losses for real time style transfer and super resolution"
    - "unsupervised cross domain image generation"
    - "neural photo editing with introspective adversarial networks"
    - "texture networks feed forward synthesis of textures and stylized images"

Connected through hyperedge HE-4 (induced by source paper "singan learning a generative model from a single natural image"):
  with members:
    - "surprising effectiveness of few image unsupervised feature learning"
    - "non stationary texture synthesis by adversarial expansion"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "local aggregation for unsupervised learning of visual embeddings"):
  with members:
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "improving generalization via scalable neighborhood component analysis"
    - "representation learning with contrastive predictive coding"
    - "self supervised feature learning by learning to spot artifacts"

Connected through hyperedge HE-6 (induced by source paper "gated gan adversarial gated networks for multi collection style transfer"):
  with members:
    - "activation maximization generative adversarial nets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "style transfer via texture synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "auto embedding generative adversarial networks for high resolution image synthesis"):
  with members:
    - "esrgan enhanced super resolution generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on self modulation for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "texture mixer a network for controllable synthesis and interpolation of texture"):
  with members:
    - "non stationary texture synthesis by adversarial expansion"
    - "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"

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)