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: "exploring the neural algorithm of artistic style"
  Abstract: We explore the method of style transfer presented in the article "A Neural Algorithm of Artistic Style" by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge (arXiv:1508.06576). #R##N#We first demonstrate the power of the suggested style space on a few examples. We then vary different hyper-parameters and program properties that were not discussed in the original paper, among which are the recognition network used, starting point of the gradient descent and different ways to partition style and content layers. We also give a brief comparison of some of the existing algorithm implementations and deep learning frameworks used. #R##N#To study the style space further we attempt to generate synthetic images by maximizing a single entry in one of the Gram matrices $\mathcal{G}_l$ and some interesting results are observed. Next, we try to mimic the sparsity and intensity distribution of Gram matrices obtained from a real painting and generate more complex textures. #R##N#Finally, we propose two new style representations built on top of network's features and discuss how one could be used to achieve local and potentially content-aware style transfer.

Connected through hyperedge HE-1 (induced by source paper "applying visual domain style transfer and texture synthesis techniques to audio insights and challenges"):
  with members:
    - "audio style transfer"
    - "neural style transfer for audio spectograms"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "neural style transfer a review"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-2 (induced by source paper "style transfer based image synthesis as an efficient regularization technique in deep learning"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "text to image synthesis using generative adversarial networks"
    - "staingan stain style transfer for digital histological images"
    - "gans for medical image analysis"
    - "random erasing data augmentation"
    - "data driven color augmentation techniques for deep skin image analysis"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "improving style transfer with calibrated metrics"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "demystifying neural style transfer"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deep photo style transfer"
    - "characterizing and improving stability in neural style transfer"
    - "neural style transfer a review"
    - "universal style transfer via feature transforms"

Connected through hyperedge HE-4 (induced by source paper "neural abstract style transfer for chinese traditional painting"):
  with members:
    - "stereoscopic neural style transfer"
    - "synthesizing filamentary structured images with gans"
    - "bringing impressionism to life with neural style transfer in come swim"
    - "deep photo style transfer"
    - "laplacian steered neural style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stylebank an explicit representation for neural image style transfer"
    - "auto painter cartoon image generation from sketch by using conditional generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "artistic style transfer for videos and spherical images"):
  with members:
    - "multi style generative network for real time transfer"
    - "deep photo style transfer"
    - "exploring the structure of a real time arbitrary neural artistic stylization network"
    - "characterizing and improving stability in neural style transfer"
    - "coherent online video style transfer"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-6 (induced by source paper "an optimized architecture for unpaired image to image translation"):
  with members:
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "preserving color in neural artistic style transfer"
    - "a neural algorithm of artistic style"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "a comprehensive comparison between neural style transfer and universal style transfer"):
  with members:
    - "universal style transfer via feature transforms"
    - "perceptual losses for real time style transfer and super resolution"
    - "preserving color in neural artistic style transfer"
    - "a neural algorithm of artistic style"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "a powerful generative model using random weights for the deep image representation"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "understanding neural networks through deep visualization"
    - "inverting visual representations with convolutional networks"
    - "a neural algorithm of artistic style"
    - "deep residual learning for image recognition"

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)