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: "learning linear transformations for fast arbitrary style transfer"
  Abstract: Given a random pair of images, an arbitrary style transfer method extracts the feel from the reference image to synthesize an output based on the look of the other content image. Recent arbitrary style transfer methods transfer second order statistics from reference image onto content image via a multiplication between content image features and a transformation matrix, which is computed from features with a pre-determined algorithm. These algorithms either require computationally expensive operations, or fail to model the feature covariance and produce artifacts in synthesized images. Generalized from these methods, in this work, we derive the form of transformation matrix theoretically and present an arbitrary style transfer approach that learns the transformation matrix with a feed-forward network. Our algorithm is highly efficient yet allows a flexible combination of multi-level styles while preserving content affinity during style transfer process. We demonstrate the effectiveness of our approach on four tasks: artistic style transfer, video and photo-realistic style transfer as well as domain adaptation, including comparisons with the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "imaginenet restyling apps using neural style transfer"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "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-2 (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"
    - "stereoscopic neural style transfer"
    - "accurate light field depth estimation with superpixel regularization over partially occluded regions"
    - "characterizing and improving stability in neural style transfer"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "universal style transfer via feature transforms"

Connected through hyperedge HE-3 (induced by source paper "style mixer semantic aware multi style transfer network"):
  with members:
    - "attention aware multi stroke style transfer"
    - "arbitrary style transfer with style attentional networks"
    - "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-4 (induced by source paper "styleremix an interpretable representation for neural image style transfer"):
  with members:
    - "a style aware content loss for real time hd 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-5 (induced by source paper "diversified arbitrary style transfer via deep feature perturbation"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "arbitrary style transfer with deep feature reshuffle"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "multi style generative network for real time transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"

Connected through hyperedge HE-6 (induced by source paper "etnet error transition network for arbitrary style transfer"):
  with members:
    - "attention aware multi stroke style transfer"
    - "a style aware content loss for real time hd 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-7 (induced by source paper "dynamic instance normalization for arbitrary style transfer"):
  with members:
    - "few shot unsupervised image to image translation"
    - "multimodal unsupervised image to image translation"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "demystifying neural style transfer"
    - "multi style generative network for real time transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "a closed form solution to universal style transfer"):
  with members:
    - "artistic style transfer for videos and spherical images"
    - "stereoscopic neural style transfer"
    - "arbitrary style transfer with deep feature reshuffle"
    - "demystifying neural style transfer"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "multi style generative network for real time transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "neural style transfer a review"

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)