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: "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
  Abstract: The recent work of Gatys et al., who characterized the style of an image by the statistics of convolutional neural network filters, ignited a renewed interest in the texture generation and image stylization problems. While their image generation technique uses a slow optimization process, recently several authors have proposed to learn generator neural networks that can produce similar outputs in one quick forward pass. While generator networks are promising, they are still inferior in visual quality and diversity compared to generation-by-optimization. In this work, we advance them in two significant ways. First, we introduce an instance normalization module to replace batch normalization with significant improvements to the quality of image stylization. Second, we improve diversity by introducing a new learning formulation that encourages generators to sample unbiasedly from the Julesz texture ensemble, which is the equivalence class of all images characterized by certain filter responses. Together, these two improvements take feed forward texture synthesis and image stylization much closer to the quality of generation-via-optimization, while retaining the speed advantage.

Connected through hyperedge HE-1 (induced by source paper "deep domain adversarial image generation for domain generalisation"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "model vulnerability to distributional shifts over image transformation sets"
    - "episodic training for domain generalization"
    - "domain generalization by solving jigsaw puzzles"
    - "torchreid a library for deep learning person re identification in pytorch"
    - "learning generalisable omni scale representations for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"

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 "parameter free style projection for arbitrary style transfer"):
  with members:
    - "stylenas an empirical study of neural architecture search to uncover surprisingly fast end to end universal style transfer networks"
    - "fast universal style transfer for artistic and photorealistic rendering"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "a style based generator architecture for generative adversarial networks"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-4 (induced by source paper "applying visual domain style transfer and texture synthesis techniques to audio insights and challenges"):
  with members:
    - "audio style transfer"
    - "neural style transfer for audio spectograms"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "neural style transfer a review"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (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"
    - "learning texture manifolds with the periodic spatial gan"
    - "universal style transfer via feature transforms"

Connected through hyperedge HE-6 (induced by source paper "structured gans"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "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"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "style transfer for light field photography"):
  with members:
    - "a content transformation block for image style transfer"
    - "style transfer by relaxed optimal transport and self similarity"
    - "artistic style transfer for videos and spherical images"
    - "learning linear transformations for fast arbitrary style transfer"
    - "stereoscopic neural style transfer"
    - "accurate light field depth estimation with superpixel regularization over partially occluded regions"
    - "characterizing and improving stability in neural style transfer"
    - "universal style transfer via feature transforms"

Connected through hyperedge HE-8 (induced by source paper "p 2 gan efficient style transfer using single style image"):
  with members:
    - "attention aware multi stroke style transfer"
    - "gated gan adversarial gated networks for multi collection style transfer"
    - "arbitrary style transfer with deep feature reshuffle"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "neural style transfer a review"
    - "visual attribute transfer through deep image analogy"
    - "stylebank an explicit representation for neural image style transfer"
    - "perceptual losses for real time style transfer and super resolution"

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)