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: "neural style transfer for audio spectograms"
  Abstract: There has been fascinating work on creating artistic transformations of images by Gatys. This was revolutionary in how we can in some sense alter the 'style' of an image while generally preserving its 'content'. In our work, we present a method for creating new sounds using a similar approach, treating it as a style-transfer problem, starting from a random-noise input signal and iteratively using back-propagation to optimize the sound to conform to filter-outputs from a pre-trained neural architecture of interest. #R##N#For demonstration, we investigate two different tasks, resulting in bandwidth expansion/compression, and timbral transfer from singing voice to musical instruments. A feature of our method is that a single architecture can generate these different audio-style-transfer types using the same set of parameters which otherwise require different complex hand-tuned diverse signal processing pipelines.

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"
    - "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"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of audio perception for robotics applications learning to project data to t sne umap space"):
  with members:
    - "neuralogram a deep neural network based representation for audio signals"
    - "end to end spoken language translation"
    - "audio linguistic embeddings for spoken sentences"
    - "learning to model aspects of hearing perception using neural loss functions"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "conditional end to end audio transforms"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "look listen and learn"

Connected through hyperedge HE-3 (induced by source paper "neuralogram a deep neural network based representation for audio signals"):
  with members:
    - "audio linguistic embeddings for spoken sentences"
    - "conditional end to end audio transforms"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "deep contextualized word representations"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "voxceleb2 deep speaker recognition"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-4 (induced by source paper "learning to model aspects of hearing perception using neural loss functions"):
  with members:
    - "end to end spoken language translation"
    - "audio linguistic embeddings for spoken sentences"
    - "conditional end to end audio transforms"
    - "quality net an end to end non intrusive speech quality assessment model based on blstm"
    - "audio adversarial examples targeted attacks on speech to text"
    - "learned perceptual image enhancement"
    - "tacotron towards end to end speech synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"):
  with members:
    - "modulated variational auto encoders for many to many musical timbre transfer"
    - "augmented cyclic adversarial learning for low resource domain adaptation"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "music style transfer issues a position paper"
    - "a universal music translation network"
    - "unsupervised cipher cracking using discrete gans"
    - "symbolic music genre transfer with cyclegan"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "play as you like timbre enhanced multi modal music style transfer"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "multimodal unsupervised image to image translation"
    - "conditional end to end audio transforms"
    - "singing style transfer using cycle consistent boundary equilibrium generative adversarial networks"
    - "music style transfer issues a position paper"
    - "a universal music translation network"
    - "generative face completion"

Connected through hyperedge HE-7 (induced by source paper "the effect of explicit structure encoding of deep neural networks for symbolic music generation"):
  with members:
    - "counterpoint by convolution"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "music style transfer issues a position paper"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "deep learning techniques for music generation a survey"
    - "a unit selection methodology for music generation using deep neural networks"
    - "fast wavenet generation algorithm"

Connected through hyperedge HE-8 (induced by source paper "music style transfer a position paper"):
  with members:
    - "counterpoint by convolution"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "deepj style specific music generation"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "jade joint autoencoders for dis entanglement"
    - "deep learning techniques for music generation a survey"
    - "disentangled representations for manipulation of sentiment in text"
    - "neural translation of musical style"

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.SD(Sound)