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: "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
  Abstract: In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness. In principle, one could apply image-based style transfer techniques to a time-frequency representation of an audio signal, but this depends on having a representation that allows independent manipulation of timbre as well as high-quality waveform generation. We introduce TimbreTron, a method for musical timbre transfer which applies "image" domain style transfer to a time-frequency representation of the audio signal, and then produces a high-quality waveform using a conditional WaveNet synthesizer. We show that the Constant Q Transform (CQT) representation is particularly well-suited to convolutional architectures due to its approximate pitch equivariance. Based on human perceptual evaluations, we confirmed that TimbreTron recognizably transferred the timbre while otherwise preserving the musical content, for both monophonic and polyphonic samples.

Connected through hyperedge HE-1 (induced by source paper "deep learning for audio signal processing"):
  with members:
    - "piano genie"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "toward domain invariant speech recognition via large scale training"
    - "efficient neural audio synthesis"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "supervised speech separation based on deep learning an overview"

Connected through hyperedge HE-2 (induced by source paper "learning disentangled representations of timbre and pitch for musical instrument sounds using gaussian mixture variational autoencoders"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "modulated variational auto encoders for many to many musical timbre transfer"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "learning disentangled representations for timber and pitch in music audio"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "a universal music translation network"
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

Connected through hyperedge HE-3 (induced by source paper "adversarial generation of time frequency features with application in audio synthesis"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "a context encoder for audio inpainting"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "large scale gan training for high fidelity natural image synthesis"
    - "adversarial audio synthesis"
    - "improving dnn based music source separation using phase features"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-4 (induced by source paper "a style transfer approach to source separation"):
  with members:
    - "cyclegan vc2 improved cyclegan based non parallel voice conversion"
    - "unsupervised training of a deep clustering model for multichannel blind source separation"
    - "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel speech separation"
    - "unsupervised training of neural mask based beamforming"
    - "audio style transfer"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "sdr half baked or well done"
    - "unsupervised deep clustering for source separation direct learning from mixtures using spatial information"

Connected through hyperedge HE-5 (induced by source paper "artist style transfer via quadratic potential"):
  with members:
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "gan qp a novel gan framework without gradient vanishing and lipschitz constraint"
    - "improving gans using optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a fully progressive approach to single image super resolution"
    - "a style based generator architecture for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "face super resolution through wasserstein gans"

Connected through hyperedge HE-6 (induced by source paper "environment sound classification using multiple feature channels and attention based deep convolutional neural network"):
  with members:
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "multi stream network with temporal attention for environmental sound classification"
    - "randomly weighted cnns for music audio classification"
    - "study of residual networks for image recognition"
    - "deep convolutional neural network with mixup for environmental sound classification"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "learning from between class examples for deep sound recognition"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-7 (induced by source paper "neural drum machine an interactive system for real time synthesis of drum sounds"):
  with members:
    - "variation network learning high level attributes for controlled input manipulation"
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "generative timbre spaces regularizing variational auto encoders with perceptual metrics"
    - "adversarial audio synthesis"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "checkerboard artifact free sub pixel convolution a note on sub pixel convolution resize convolution and convolution resize"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-8 (induced by source paper "exploring conditioning for generative music systems with human interpretable controls"):
  with members:
    - "piano genie"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "this time with feeling learning expressive musical performance"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "music transformer"
    - "a functional taxonomy of music generation systems"
    - "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)