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: "a style aware content loss for real time hd style transfer"
  Abstract: Recently, style transfer has received a lot of attention. While much of this research has aimed at speeding up processing, the approaches are still lacking from a principled, art historical standpoint: a style is more than just a single image or an artist, but previous work is limited to only a single instance of a style or shows no benefit from more images. Moreover, previous work has relied on a direct comparison of art in the domain of RGB images or on CNNs pre-trained on ImageNet, which requires millions of labeled object bounding boxes and can introduce an extra bias, since it has been assembled without artistic consideration. To circumvent these issues, we propose a style-aware content loss, which is trained jointly with a deep encoder-decoder network for real-time, high-resolution stylization of images and videos. We propose a quantitative measure for evaluating the quality of a stylized image and also have art historians rank patches from our approach against those from previous work. These and our qualitative results ranging from small image patches to megapixel stylistic images and videos show that our approach better captures the subtle nature in which a style affects content.

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"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"

Connected through hyperedge HE-2 (induced by source paper "a content transformation block for image style transfer"):
  with members:
    - "adjustable real time style transfer"
    - "artistic style transfer for videos and spherical images"
    - "generative adversarial networks an overview"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "diversified texture synthesis with feed forward networks"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "characterizing and improving stability in neural style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (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"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "synthesizing programs for images using reinforced adversarial learning"

Connected through hyperedge HE-4 (induced by source paper "multi attribute guided painting generation"):
  with members:
    - "automated pebble mosaic stylization of images"
    - "arbitrary style transfer with style attentional networks"
    - "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-5 (induced by source paper "conditional neural style transfer with peer regularized feature transform"):
  with members:
    - "few shot unsupervised image to image translation"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "the relativistic discriminator a key element missing from standard gan"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "peernets exploiting peer wisdom against adversarial attacks"

Connected through hyperedge HE-6 (induced by source paper "styleremix an interpretable representation for neural image style transfer"):
  with members:
    - "learning linear transformations for fast arbitrary style transfer"
    - "arbitrary style transfer with deep feature reshuffle"
    - "multi style generative network for real time transfer"
    - "diversified texture synthesis with feed forward networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "exploring the structure of a real time arbitrary neural artistic stylization network"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "visual attribute transfer through deep image analogy"

Connected through hyperedge HE-7 (induced by source paper "etnet error transition network for arbitrary style transfer"):
  with members:
    - "attention aware multi stroke style transfer"
    - "learning linear transformations for fast arbitrary style transfer"
    - "difnet semantic segmentation by diffusion networks"
    - "style separation and synthesis via generative adversarial networks"
    - "deep back projection networks for super resolution"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "nonlocal neural networks nonlocal diffusion and nonlocal modeling"
    - "meta networks for neural style transfer"

Connected through hyperedge HE-8 (induced by source paper "two stream fcns to balance content and style for style transfer"):
  with members:
    - "evolvement constrained adversarial learning for video style transfer"
    - "a closed form solution to photorealistic image stylization"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "photorealistic style transfer with screened poisson equation"
    - "deep photo style transfer"
    - "neural style transfer a review"
    - "multi content gan for few shot font style transfer"

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)