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: "data efficient voice cloning for neural singing synthesis"
  Abstract: There are many use cases in singing synthesis where creating voices from small amounts of data is desirable. In text-to-speech there have been several promising results that apply voice cloning techniques to modern deep learning based models. In this work, we adapt one such technique to the case of singing synthesis. By leveraging data from many speakers to first create a multispeaker model, small amounts of target data can then efficiently adapt the model to new unseen voices. We evaluate the system using listening tests across a number of different use cases, languages and kinds of data.

Connected through hyperedge HE-1 (induced by source paper "score and lyrics free singing voice generation"):
  with members:
    - "dilated convolution with dilated gru for music source separation"
    - "high fidelity speech synthesis with adversarial networks"
    - "adversarially trained end to end korean singing voice synthesis system"
    - "gansynth adversarial neural audio synthesis"
    - "an overview of lead and accompaniment separation in music"
    - "group normalization"
    - "revisiting singing voice detection a quantitative review and the future outlook"
    - "sing symbol to instrument neural generator"

Connected through hyperedge HE-2 (induced by source paper "unsupervised singing voice conversion"):
  with members:
    - "unsupervised polyglot text to speech"
    - "sample efficient adaptive text to speech"
    - "fitting new speakers based on a short untranscribed sample"
    - "a universal music translation network"
    - "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-3 (induced by source paper "wgansing a multi voice singing voice synthesizer based on the wasserstein gan"):
  with members:
    - "harmonizing maximum likelihood with gans for multimodal conditional generation"
    - "gansynth adversarial neural audio synthesis"
    - "adversarial audio synthesis"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "a neural parametric singing synthesizer"
    - "statistical parametric speech synthesis using generative adversarial networks under a multi task learning framework"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "improved techniques for training gans"

Connected through hyperedge HE-4 (induced by source paper "learning singing from speech"):
  with members:
    - "unsupervised singing voice conversion"
    - "durian duration informed attention network for multimodal synthesis"
    - "efficient neural audio synthesis"
    - "tacotron towards end to end speech synthesis"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-5 (induced by source paper "disentangling timbre and singing style with multi singer singing synthesis system"):
  with members:
    - "wgansing a multi voice singing voice synthesizer based on the wasserstein gan"
    - "adversarially trained end to end korean singing voice synthesis system"
    - "sample efficient adaptive text to speech"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "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.SD(Sound)