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: "texture synthesis using shallow convolutional networks with random filters"
  Abstract: Here we demonstrate that the feature space of random shallow convolutional neural networks (CNNs) can serve as a surprisingly good model of natural textures. Patches from the same texture are consistently classified as being more similar then patches from different textures. Samples synthesized from the model capture spatial correlations on scales much larger then the receptive field size, and sometimes even rival or surpass the perceptual quality of state of the art texture models (but show less variability). The current state of the art in parametric texture synthesis relies on the multi-layer feature space of deep CNNs that were trained on natural images. Our finding suggests that such optimized multi-layer feature spaces are not imperative for texture modeling. Instead, much simpler shallow and convolutional networks can serve as the basis for novel texture synthesis algorithms.

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 "macrocanonical models for texture synthesis"):
  with members:
    - "a survey of exemplar based texture synthesis"
    - "texture synthesis through convolutional neural networks and spectrum constraints"
    - "controlling perceptual factors in neural style transfer"
    - "texture synthesis with spatial generative adversarial networks"
    - "texture synthesis using convolutional neural networks"
    - "learning frame models using cnn filters"
    - "very deep convolutional networks for large scale image recognition"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-3 (induced by source paper "mechanisms of artistic creativity in deep learning neural networks"):
  with members:
    - "applying visual domain style transfer and texture synthesis techniques to audio insights and challenges"
    - "one pixel attack for fooling deep neural networks"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "a neural representation of sketch drawings"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "very deep convolutional networks for large scale image recognition"
    - "intriguing properties of neural networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "how big can style be addressing high dimensionality for recommending with style"):
  with members:
    - "style2vec representation learning for fashion items from style sets"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"
    - "a neural algorithm of artistic style"
    - "texture synthesis using convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "sound texture synthesis using ri spectrograms"):
  with members:
    - "sound texture synthesis using convolutional neural networks"
    - "gansynth adversarial neural audio synthesis"
    - "texture synthesis using convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "sound texture synthesis using convolutional neural networks"):
  with members:
    - "synthesizing diverse high quality audio textures"
    - "texture synthesis using convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "adversarial reprogramming of neural networks"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "adversarial logit pairing"
    - "adversarial attacks on neural network policies"
    - "robust physical world attacks on deep learning models"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-8 (induced by source paper "synthesizing diverse high quality audio textures"):
  with members:
    - "audio style transfer"
    - "understanding deep representations through random weights"
    - "on using backpropagation for speech texture generation and voice conversion"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "improved techniques for training gans"
    - "a powerful generative model using random weights for the deep image representation"
    - "a learned representation for artistic style"
    - "wavenet a generative model for raw audio"

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)