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: "neural style transfer a review"
  Abstract: The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "collaborative distillation for ultra resolution universal style transfer"):
  with members:
    - "relational knowledge distillation"
    - "multimodal style transfer via graph cuts"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unpaired high resolution and scalable style transfer using generative adversarial networks"
    - "a style aware content loss for real time hd style transfer"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-2 (induced by source paper "sketch to art synthesizing stylized art images from sketches"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "the shape of art history in the eyes of the machine"
    - "style and content disentanglement in generative adversarial networks"

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 "interactive neural style transfer with artists"):
  with members:
    - "a content transformation block for image style transfer"
    - "style transfer by relaxed optimal transport and self similarity"
    - "dialog on a canvas with a machine"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "wasserstein style transfer"
    - "multimodal style transfer via graph cuts"
    - "a style aware content loss for real time hd style transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-5 (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"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-6 (induced by source paper "separating content from style using adversarial learning for recognizing text in the wild"):
  with members:
    - "controllable artistic text style transfer via shape matching gan"
    - "scene text recognition from two dimensional perspective"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "began boundary equilibrium generative adversarial networks"
    - "multi content gan for few shot font style transfer"
    - "focusing attention towards accurate text recognition in natural images"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "machine learning based co creative design framework"):
  with members:
    - "friend collaborator student manager how design of an ai driven game level editor affects creators"
    - "toward a neuro inspired creative decoder"
    - "design design inspiration from generative networks"
    - "co creative level design via machine learning"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "styling with attention to details"
    - "generative image inpainting with contextual attention"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"

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"
    - "visual attribute transfer through deep image analogy"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "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)