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: "a universal music translation network"
  Abstract: We present a method for translating music across musical instruments, genres, and styles. This method is based on a multi-domain wavenet autoencoder, with a shared encoder and a disentangled latent space that is trained end-to-end on waveforms. Employing a diverse training dataset and large net capacity, the domain-independent encoder allows us to translate even from musical domains that were not seen during training. The method is unsupervised and does not rely on supervision in the form of matched samples between domains or musical transcriptions. We evaluate our method on NSynth, as well as on a dataset collected from professional musicians, and achieve convincing translations, even when translating from whistling, potentially enabling the creation of instrumental music by untrained humans.

Connected through hyperedge HE-1 (induced by source paper "latent translation crossing modalities by bridging generative models"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "video to video synthesis"
    - "twin gan unpaired cross domain image translation with weight sharing gans"
    - "adversarial audio synthesis"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "generative modeling using the sliced wasserstein distance"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion"):
  with members:
    - "unsupervised singing voice conversion"
    - "a rad approach to deep mixture models"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "emerging convolutions for generative normalizing flows"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "conditional recurrent flow conditional generation of longitudinal samples with applications to neuroimaging"
    - "neural voice cloning with a few samples"

Connected through hyperedge HE-3 (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"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "learning disentangled representations for timber and pitch in music audio"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

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 "encoding musical style with transformer autoencoders"):
  with members:
    - "exploring conditioning for generative music systems with human interpretable controls"
    - "learning to groove with inverse sequence transformations"
    - "improving automatic jazz melody generation by transfer learning techniques"
    - "counterpoint by convolution"
    - "this time with feeling learning expressive musical performance"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "self attention with relative position representations"
    - "discrete autoencoders for sequence models"

Connected through hyperedge HE-6 (induced by source paper "unsupervised singing voice conversion"):
  with members:
    - "unsupervised polyglot text to speech"
    - "data efficient voice cloning for neural singing synthesis"
    - "sample efficient adaptive text to speech"
    - "fitting new speakers based on a short untranscribed sample"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "mixup beyond empirical risk minimization"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"

Connected through hyperedge HE-7 (induced by source paper "tts skins speaker conversion via asr"):
  with members:
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "sample efficient adaptive text to speech"
    - "fitting new speakers based on a short untranscribed sample"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "wav2letter an end to end convnet based speech recognition system"
    - "deep voice 2 multi speaker neural text to speech"

Connected through hyperedge HE-8 (induced by source paper "deep learning based automatic downbeat tracking a brief review"):
  with members:
    - "madmom a new python audio and music signal processing library"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "robust downbeat tracking using an ensemble of convolutional networks"
    - "music transcription modelling and composition using deep learning"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "improvements to deep convolutional neural networks for lvcsr"

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)