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 through convolutional neural networks and spectrum constraints"
  Abstract: This paper presents a significant improvement for the synthesis of texture images using convolutional neural networks (CNNs), making use of constraints on the Fourier spectrum of the results. More precisely, the texture synthesis is regarded as a constrained optimization problem, with constraints conditioning both the Fourier spectrum and statistical features learned by CNNs. In contrast with existing methods, the presented method inherits from previous CNN approaches the ability to depict local structures and fine scale details, and at the same time yields coherent large scale structures, even in the case of quasi-periodic images. This is done at no extra computational cost. Synthesis experiments on various images show a clear improvement compared to a recent state-of-the art method relying on CNN constraints only.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "conditional generative convnets for exemplar based texture synthesis"):
  with members:
    - "learning the synthesizability of dynamic texture samples"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "spectral normalization for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "a survey of exemplar based texture synthesis"
    - "texture characterization using shape co occurrence patterns"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "macrocanonical models for texture synthesis"):
  with members:
    - "a survey of exemplar based texture synthesis"
    - "texture synthesis using shallow convolutional networks with random filters"
    - "controlling perceptual factors in neural style transfer"
    - "texture synthesis with spatial generative adversarial networks"
    - "texture synthesis using convolutional neural networks"
    - "learning frame models using cnn filters"
    - "very deep convolutional networks for large scale image recognition"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-4 (induced by source paper "one shot texture segmentation"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "one shot segmentation in clutter"
    - "prototypical networks for few shot learning"
    - "mask r cnn"
    - "few shot learning through an information retrieval lens"
    - "semantic amodal segmentation"
    - "learning to segment object candidates"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "texture mixing by interpolating deep statistics via gaussian models"):
  with members:
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "a learned representation for artistic style"
    - "texture synthesis with spatial generative adversarial networks"
    - "texture synthesis using convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "image inpainting for high resolution textures using cnn texture synthesis"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "incorporating long range consistency in cnn based texture generation"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "context encoders feature learning by inpainting"
    - "texture synthesis using convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"

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)