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: "arbitrary style transfer with style attentional networks"
  Abstract: Arbitrary style transfer aims to synthesize a content image with the style of an image to create a third image that has never been seen before. Recent arbitrary style transfer algorithms find it challenging to balance the content structure and the style patterns. Moreover, simultaneously maintaining the global and local style patterns is difficult due to the patch-based mechanism. In this paper, we introduce a novel style-attentional network (SANet) that efficiently and flexibly integrates the local style patterns according to the semantic spatial distribution of the content image. A new identity loss function and multi-level feature embeddings enable our SANet and decoder to preserve the content structure as much as possible while enriching the style patterns. Experimental results demonstrate that our algorithm synthesizes stylized images in real-time that are higher in quality than those produced by the state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "swaptext image based texts transfer in scenes"):
  with members:
    - "controllable artistic text style transfer via shape matching gan"
    - "ga dan geometry aware domain adaptation network for scene text detection and recognition"
    - "symmetry constrained rectification network for scene text recognition"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "spatial fusion gan for image synthesis"
    - "image inpainting via generative multi column convolutional neural networks"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-2 (induced by source paper "multi attribute guided painting generation"):
  with members:
    - "automated pebble mosaic stylization of images"
    - "a style aware content loss for real time hd style transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-3 (induced by source paper "neural arbitrary style transfer for portrait images using the attention mechanism"):
  with members:
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "a neural algorithm of artistic style"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "unsupervised image to image translation with self attention networks"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "non local neural networks"
    - "combogan unrestrained scalability for image domain translation"

Connected through hyperedge HE-5 (induced by source paper "style mixer semantic aware multi style transfer network"):
  with members:
    - "attention aware multi stroke style transfer"
    - "learning linear transformations for fast arbitrary style transfer"
    - "metastyle three way trade off among speed flexibility and quality in neural style transfer"
    - "arbitrary style transfer with deep feature reshuffle"
    - "the contextual loss for image transformation with non aligned data"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "non local neural networks"
    - "demystifying neural style transfer"

Connected through hyperedge HE-6 (induced by source paper "marionette few shot face reenactment preserving identity of unseen targets"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "attention based fusion for multi source human image generation"
    - "few shot adversarial learning of realistic neural talking head models"
    - "few shot unsupervised image to image translation"
    - "faceswapnet landmark guided many to many face reenactment"
    - "cgans with projection discriminator"
    - "soft gated warping gan for pose guided person image synthesis"
    - "synthesizing images of humans in unseen poses"

Connected through hyperedge HE-7 (induced by source paper "pair wise exchangeable feature extraction for arbitrary style transfer"):
  with members:
    - "multi marginal wasserstein gan"
    - "attention aware multi stroke style transfer"
    - "etnet error transition network for arbitrary style transfer"
    - "adversarial learning with local coordinate coding"
    - "a style aware content loss for real time hd style transfer"
    - "multimodal unsupervised image to image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "style separation and synthesis via generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "efanet exchangeable feature alignment network for arbitrary style transfer"):
  with members:
    - "multi marginal wasserstein gan"
    - "attention aware multi stroke style transfer"
    - "etnet error transition network for arbitrary style transfer"
    - "a style aware content loss for real time hd style transfer"
    - "multimodal unsupervised image to image translation"
    - "style separation and synthesis via generative adversarial networks"
    - "dida disentangled synthesis for domain adaptation"
    - "avatar net multi scale zero shot style transfer by feature decoration"

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)