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: "adversarially trained end to end korean singing voice synthesis system"
  Abstract: In this paper, we propose an end-to-end Korean singing voice synthesis system from lyrics and a symbolic melody using the following three novel approaches: 1) phonetic enhancement masking, 2) local conditioning of text and pitch to the super-resolution network, and 3) conditional adversarial training. The proposed system consists of two main modules; a mel-synthesis network that generates a mel-spectrogram from the given input information, and a super-resolution network that upsamples the generated mel-spectrogram into a linear-spectrogram. In the mel-synthesis network, phonetic enhancement masking is applied to generate implicit formant masks solely from the input text, which enables a more accurate phonetic control of singing voice. In addition, we show that two other proposed methods -- local conditioning of text and pitch, and conditional adversarial training -- are crucial for a realistic generation of the human singing voice in the super-resolution process. Finally, both quantitative and qualitative evaluations are conducted, confirming the validity of all proposed methods.

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"
    - "data efficient voice cloning for neural singing synthesis"
    - "high fidelity speech synthesis with adversarial networks"
    - "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 "sequence to sequence singing synthesis using the feed forward transformer"):
  with members:
    - "singing voice synthesis using deep autoregressive neural networks for acoustic modeling"
    - "fastspeech fast robust and controllable text to speech"
    - "parallel neural text to speech"
    - "wgansing a multi voice singing voice synthesizer based on the wasserstein gan"
    - "waveglow a flow based generative network for speech synthesis"
    - "neural speech synthesis with transformer network"
    - "self attentional acoustic models"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"

Connected through hyperedge HE-3 (induced by source paper "mellotron multispeaker expressive voice synthesis by conditioning on rhythm pitch and global style tokens"):
  with members:
    - "libritts a corpus derived from librispeech for text to speech"
    - "waveglow a flow based generative network for speech synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "state of the art speech recognition with sequence to sequence models"
    - "deep voice 3 2000 speaker neural text to speech"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "disentangling timbre and singing style with multi singer singing synthesis system"):
  with members:
    - "data efficient voice cloning for neural singing synthesis"
    - "wgansing a multi voice singing voice synthesizer based on the wasserstein gan"
    - "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)