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: "ganosaic mosaic creation with generative texture manifolds"
  Abstract: This paper presents a novel framework for generating texture mosaics with convolutional neural networks. Our method is called GANosaic and performs optimization in the latent noise space of a generative texture model, which allows the transformation of a content image into a mosaic exhibiting the visual properties of the underlying texture manifold. To represent that manifold, we use a state-of-the-art generative adversarial method for texture synthesis, which can learn expressive texture representations from data and produce mosaic images with very high resolution. This fully convolutional model generates smooth (without any visible borders) mosaic images which morph and blend different textures locally. In addition, we develop a new type of differentiable statistical regularization appropriate for optimization over the prior noise space of the PSGAN model.

Connected through hyperedge HE-1 (induced by source paper "tilegan synthesis of large scale non homogeneous textures"):
  with members:
    - "multi chart generative surface modeling"
    - "carigans unpaired photo to caricature translation"
    - "copy the old or paint anew an adversarial framework for non parametric image stylization"
    - "non stationary texture synthesis by adversarial expansion"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "transform the set memory attentive generation of guided and unguided image collages"):
  with members:
    - "copy the old or paint anew an adversarial framework for non parametric image stylization"
    - "visual attribute transfer through deep image analogy"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "image to image translation with conditional adversarial networks"
    - "texture synthesis with spatial generative adversarial networks"
    - "a neural algorithm of artistic style"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "spatial transformer networks"

Connected through hyperedge HE-3 (induced by source paper "computational parquetry fabricated style transfer with wood pixels"):
  with members:
    - "neural style transfer a review"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "u net convolutional networks for biomedical image segmentation"
    - "texture synthesis using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "first order generative adversarial networks"):
  with members:
    - "on convergence and stability of gans"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "which training methods for gans do actually converge"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "the numerics of gans"
    - "approximation and convergence properties of generative adversarial learning"
    - "gradient descent gan optimization is locally stable"
    - "learning texture manifolds with the periodic spatial gan"

Connected through hyperedge HE-5 (induced by source paper "copy the old or paint anew an adversarial framework for non parametric image stylization"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "toward multimodal image to image translation"
    - "learning texture manifolds with the periodic spatial gan"
    - "texture synthesis with spatial generative adversarial networks"
    - "a neural algorithm of artistic style"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

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)