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: "stroke controllable fast style transfer with adaptive receptive fields"
  Abstract: Recently, in the community of Neural Style Transfer, several algorithms are proposed to transfer an artistic style in real-time, which is known as Fast Style Transfer. However, controlling the stroke size in stylized results still remains an open challenge. To achieve controllable stroke sizes, several attempts were made including training multiple models and resizing the input image in a variety of scales, respectively. However, their results are not promising regarding the efficiency and quality. In this paper, we present a stroke controllable style transfer network that incorporates different stroke sizes into one single model. Firstly, by analyzing the factors that influence the stroke size, we adopt the idea that both the receptive field and the style image scale should be taken into consideration for most cases. Then we propose a StrokePyramid module to endow the network with adaptive receptive fields, and two training strategies to achieve faster convergence and augment new stroke sizes upon a trained model respectively. Finally, by combining the proposed runtime control techniques, our network can produce distinct stroke sizes in different output images or different spatial regions within the same output image. The experimental results demonstrate that with almost the same number of parameters as the previous Fast Style Transfer algorithm, our network can transfer an artistic style in a stroke controllable manner.

Connected through hyperedge HE-1 (induced by source paper "a style aware content loss for real time hd style transfer"):
  with members:
    - "a variational u net for conditional appearance and shape generation"
    - "generative adversarial networks an overview"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "meta networks for neural style transfer"
    - "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"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"

Connected through hyperedge HE-2 (induced by source paper "beyond textures learning from multi domain artistic images for arbitrary style transfer"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "a closed form solution to photorealistic image stylization"
    - "multimodal unsupervised image to image translation"
    - "the effectiveness of instance normalization a strong baseline for single image dehazing"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "multi style generative network for real time transfer"

Connected through hyperedge HE-3 (induced by source paper "neural style transfer a review"):
  with members:
    - "neural style transfer for audio spectograms"
    - "artistic style transfer for videos and spherical images"
    - "a closed form solution to photorealistic image stylization"
    - "multimodal unsupervised image to image translation"
    - "stereoscopic neural style transfer"
    - "the contextual loss for image transformation with non aligned data"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "demystifying neural 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)