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 closed form solution to photorealistic image stylization"
  Abstract: Photorealistic image style transfer algorithms aim at stylizing a content photo using the style of a reference photo with the constraint that the stylized photo should remains photorealistic. While several methods exist for this task, they tend to generate spatially inconsistent stylizations with noticeable artifacts. In addition, these methods are computationally expensive, requiring several minutes to stylize a VGA photo. In this paper, we present a novel algorithm to address the limitations. The proposed algorithm consists of a stylization step and a smoothing step. While the stylization step transfers the style of the reference photo to the content photo, the smoothing step encourages spatially consistent stylizations. Unlike existing algorithms that require iterative optimization, both steps in our algorithm have closed-form solutions. Experimental results show that the stylized photos generated by our algorithm are twice more preferred by human subjects in average. Moreover, our method runs 60 times faster than the state-of-the-art approach. Code and additional results are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "learning texture invariant representation for domain adaptation of semantic segmentation"):
  with members:
    - "domain adaptation for semantic segmentation with maximum squares loss"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "domain adaptation for structured output via discriminative patch representations"
    - "significance aware information bottleneck for domain adaptive semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"

Connected through hyperedge HE-2 (induced by source paper "neural style difference transfer and its application to font generation"):
  with members:
    - "automatic generation of typographic font from small font subset"
    - "the contextual loss for image transformation with non aligned data"
    - "pyramid embedded generative adversarial network for automated font generation"
    - "contained neural style transfer for decorated logo generation"
    - "bringing impressionism to life with neural style transfer in come swim"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "deep photo style transfer"
    - "auto encoder guided gan for chinese calligraphy synthesis"

Connected through hyperedge HE-3 (induced by source paper "high resolution daytime translation without domain labels"):
  with members:
    - "end to end time lapse video synthesis from a single outdoor image"
    - "few shot unsupervised image to image translation"
    - "deep high resolution representation learning for human pose estimation"
    - "cgans with projection discriminator"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "imaginenet restyling apps using neural style transfer"):
  with members:
    - "learning linear transformations for fast arbitrary style transfer"
    - "demystifying neural style transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "photorealistic style transfer with screened poisson equation"
    - "deep photo style transfer"
    - "visual attribute transfer through deep image analogy"
    - "universal style transfer via feature transforms"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "neural voxel renderer learning an accurate and controllable rendering tool"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deferred neural rendering image synthesis using neural textures"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "neural rendering and reenactment of human actor videos"
    - "transformable bottleneck networks"
    - "differentiable surface splatting for point based geometry processing"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"

Connected through hyperedge HE-6 (induced by source paper "domain adaptation for object detection via style consistency"):
  with members:
    - "unsupervised domain adaptation using feature whitening and consensus loss"
    - "photorealistic style transfer via wavelet transforms"
    - "semantic foggy scene understanding with synthetic data"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "omnia faster r cnn detection in the wild through dataset merging and soft distillation"
    - "splat semantic pixel level adaptation transforms for detection"

Connected through hyperedge HE-7 (induced by source paper "fast universal style transfer for artistic and photorealistic rendering"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "deep layer aggregation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"

Connected through hyperedge HE-8 (induced by source paper "representation learning through latent canonicalizations"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "meta sim learning to generate synthetic datasets"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "yolov3 an incremental improvement"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "unsupervised representation learning by predicting image rotations"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"

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)