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 mixer a network for controllable synthesis and interpolation of texture"
  Abstract: This paper addresses the problem of interpolating visual textures. We formulate the problem of texture interpolation by requiring (1) by-example controllability and (2) realistic and smooth interpolation among an arbitrary number of texture samples. To solve it we propose a neural network trained simultaneously on a reconstruction task and a generation task, which can project texture examples onto a latent space where they can be linearly interpolated and reprojected back onto the image domain, thus ensuring both intuitive control and realistic results. We show several additional applications including texture brushing and texture dissolve, and show our method outperforms a number of baselines according to a comprehensive suite of metrics as well as a user study.

Connected through hyperedge HE-1 (induced by source paper "on demand solid texture synthesis using deep 3d networks"):
  with members:
    - "non stationary texture synthesis by adversarial expansion"
    - "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-2 (induced by source paper "gan leaks a taxonomy of membership inference attacks against gans"):
  with members:
    - "non stationary texture synthesis by adversarial expansion"
    - "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-3 (induced by source paper "learning a neural 3d texture space from 2d exemplars"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "texture fields learning texture representations in function space"
    - "non stationary texture synthesis by adversarial expansion"
    - "texturegan controlling deep image synthesis with texture patches"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "learning texture manifolds with the periodic spatial gan"
    - "perceptual losses for real time style transfer and super resolution"
    - "texture networks feed forward synthesis of textures and stylized images"

Connected through hyperedge HE-4 (induced by source paper "attributing fake images to gans learning and analyzing gan fingerprints"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "learning rich features for image manipulation detection"
    - "non stationary texture synthesis by adversarial expansion"
    - "do gans leave artificial fingerprints"
    - "fighting fake news image splice detection via learned self consistency"
    - "mesonet a compact facial video forgery detection network"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "faceforensics a large scale video dataset for forgery detection in human faces"

Connected through hyperedge HE-5 (induced by source paper "attributing fake images to gans analyzing fingerprints in generated images"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "learning rich features for image manipulation detection"
    - "non stationary texture synthesis by adversarial expansion"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "spectral normalization for generative adversarial networks"
    - "noiseprint a cnn based camera model fingerprint"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

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)