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: "wgansing a multi voice singing voice synthesizer based on the wasserstein gan"
  Abstract: We present a deep neural network based singing voice synthesizer, inspired by the Deep Convolutions Generative Adversarial Networks (DCGAN) architecture and optimized using the Wasserstein-GAN algorithm. We use vocoder parameters for acoustic modelling, to separate the influence of pitch and timbre. This facilitates the modelling of the large variability of pitch in the singing voice. Our network takes a block of consecutive frame-wise linguistic and fundamental frequency features, along with global singer identity as input and outputs vocoder features. For inference, sequential blocks are concatenated using an overlap-add procedure. We show that the performance of our model is comparable to the state-of-the-art and the original sample using objective metrics and a subjective listening test. We also present examples of the synthesis on a supplementary website and the source code via GitHub.

Connected through hyperedge HE-1 (induced by source paper "ddsp differentiable digital signal processing"):
  with members:
    - "universal audio synthesizer control with normalizing flows"
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "gansynth adversarial neural audio synthesis"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "sing symbol to instrument neural generator"
    - "efficient neural audio synthesis"
    - "adversarial audio synthesis"
    - "attention is all you need"

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"
    - "adversarially trained end to end korean singing voice synthesis system"
    - "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 "synthesising expressiveness in peking opera via duration informed attention network"):
  with members:
    - "singing voice synthesis using deep autoregressive neural networks for acoustic modeling"
    - "durian duration informed attention network for multimodal synthesis"
    - "efficient neural audio synthesis"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 3 2000 speaker neural text to speech"
    - "wavenet a generative model for raw audio"

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"
    - "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)