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: "content aware neural style transfer"
  Abstract: This paper presents a content-aware style transfer algorithm for paintings and photos of similar content using pre-trained neural network, obtaining better results than the previous work. In addition, the numerical experiments show that the style pattern and the content information is not completely separated by neural network.

Connected through hyperedge HE-1 (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"
    - "a closed form solution to photorealistic image stylization"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "identity preserving face recovery from stylized portraits"):
  with members:
    - "recovering faces from portraits with auxiliary facial attributes"
    - "identity preserving face recovery from portraits"
    - "a deeper look at power normalizations"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "multi style generative network for real time transfer"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-3 (induced by source paper "stada style transfer as data augmentation"):
  with members:
    - "egocentric gesture recognition for head mounted ar devices"
    - "demystifying neural style transfer"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "neural style transfer a review"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "identity preserving face recovery from portraits"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "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"
    - "diversified texture synthesis with feed forward networks"
    - "characterizing and improving stability in neural style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-5 (induced by source paper "computational decomposition of style for controllable and enhanced style transfer"):
  with members:
    - "learning to sketch with shortcut cycle consistency"
    - "sketch pix2seq a model to generate sketches of multiple categories"
    - "neural style transfer a review"
    - "perceptual losses for real time style transfer and super resolution"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "controlling perceptual factors in neural style transfer"

Connected through hyperedge HE-6 (induced by source paper "deepmovie using optical flow and deep neural networks to stylize movies"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "a neural algorithm of artistic style"
    - "visualizing and understanding deep texture representations"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "texture synthesis using convolutional neural networks"
    - "learning frame models using cnn filters"

Connected through hyperedge HE-7 (induced by source paper "exploring the neural algorithm of artistic style"):
  with members:
    - "texture networks feed forward synthesis of textures and stylized images"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "a neural algorithm of artistic style"
    - "texture synthesis using convolutional neural networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

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)