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: "son of zorn s lemma targeted style transfer using instance aware semantic segmentation"
  Abstract: Style transfer is an important task in which the style of a source image is mapped onto that of a target image. The method is useful for synthesizing derivative works of a particular artist or specific painting. This work considers targeted style transfer, in which the style of a template image is used to alter only part of a target image. For example, an artist may wish to alter the style of only one particular object in a target image without altering the object's general morphology or surroundings. This is useful, for example, in augmented reality applications (such as the recently released Pokemon GO), where one wants to alter the appearance of a single real-world object in an image frame to make it appear as a cartoon. Most notably, the rendering of real-world objects into cartoon characters has been used in a number of films and television show, such as the upcoming series Son of Zorn. We present a method for targeted style transfer that simultaneously segments and stylizes single objects selected by the user. The method uses a Markov random field model to smooth and anti-alias outlier pixels near object boundaries, so that stylized objects naturally blend into their surroundings.

Connected through hyperedge HE-1 (induced by source paper "a method for arbitrary instance style transfer"):
  with members:
    - "class based styling real time localized style transfer with semantic segmentation"
    - "path aggregation network for instance segmentation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "universal style transfer via feature transforms"
    - "stylebank an explicit representation for neural image style transfer"
    - "mask r cnn"
    - "perceptual losses for real time style transfer and super resolution"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-2 (induced by source paper "class based styling real time localized style transfer with semantic segmentation"):
  with members:
    - "dabnet depth wise asymmetric bottleneck for real time semantic segmentation"
    - "neural style transfer a review"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "perceptual losses for real time style transfer and super resolution"
    - "a neural algorithm of artistic style"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "neural style transfer a review"):
  with members:
    - "neural style transfer for audio spectograms"
    - "stroke controllable fast style transfer with adaptive receptive fields"
    - "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"

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)