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: "time domain neural audio style transfer"
  Abstract: A recently published method for audio style transfer has shown how to extend the process of image style transfer to audio. This method synthesizes audio "content" and "style" independently using the magnitudes of a short time Fourier transform, shallow convolutional networks with randomly initialized filters, and iterative phase reconstruction with Griffin-Lim. In this work, we explore whether it is possible to directly optimize a time domain audio signal, removing the process of phase reconstruction and opening up possibilities for real-time applications and higher quality syntheses. We explore a variety of style transfer processes on neural networks that operate directly on time domain audio signals and demonstrate one such network capable of audio stylization.

Connected through hyperedge HE-1 (induced by source paper "overview of tasks and investigation of subjective evaluation methods in environmental sound synthesis and conversion"):
  with members:
    - "audio style transfer"
    - "deep learning techniques for music generation a survey"
    - "visual to sound generating natural sound for videos in the wild"
    - "samplernn an unconditional end to end neural audio generation model"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-2 (induced by source paper "generating albums with samplernn to imitate metal rock and punk bands"):
  with members:
    - "efficient neural audio synthesis"
    - "nested lstms"
    - "generating black metal and math rock beyond bach beethoven and beatles"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "generating nontrivial melodies for music as a service"
    - "progressive growing of gans for improved quality stability and variation"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "deep voice real time neural text to speech"

Connected through hyperedge HE-3 (induced by source paper "a lightweight music texture transfer system"):
  with members:
    - "a universal music translation network"
    - "toward controlled generation of text"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "style transfer in text exploration and evaluation"
    - "shakespearizing modern language using copy enriched sequence to sequence models"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-4 (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.SD(Sound)