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 neural parametric singing synthesizer"
  Abstract: We present a new model for singing synthesis based on a modified version of the WaveNet architecture. Instead of modeling raw waveform, we model features produced by a parametric vocoder that separates the influence of pitch and timbre. This allows conveniently modifying pitch to match any target melody, facilitates training on more modest dataset sizes, and significantly reduces training and generation times. Our model makes frame-wise predictions using mixture density outputs rather than categorical outputs in order to reduce the required parameter count. As we found overfitting to be an issue with the relatively small datasets used in our experiments, we propose a method to regularize the model and make the autoregressive generation process more robust to prediction errors. Using a simple multi-stream architecture, harmonic, aperiodic and voiced/unvoiced components can all be predicted in a coherent manner. We compare our method to existing parametric statistical and state-of-the-art concatenative methods using quantitative metrics and a listening test. While naive implementations of the autoregressive generation algorithm tend to be inefficient, using a smart algorithm we can greatly speed up the process and obtain a system that's competitive in both speed and quality.

Connected through hyperedge HE-1 (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"
    - "data efficient voice cloning for neural singing synthesis"
    - "gansynth adversarial neural audio synthesis"
    - "adversarial audio synthesis"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "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-2 (induced by source paper "deep unsupervised drum transcription"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "towards multi instrument drum transcription"
    - "madmom a new python audio and music signal processing library"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "u net convolutional networks for biomedical image segmentation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "singing voice synthesis using deep autoregressive neural networks for acoustic modeling"):
  with members:
    - "efficient neural audio synthesis"
    - "attention is all you need"
    - "tacotron towards end to end speech synthesis"
    - "wavenet a generative model for raw audio"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "a context encoder for audio inpainting"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "introducing spain sparse audion inpainter"
    - "phase reconstruction from amplitude spectrograms based on von mises distribution deep neural network"
    - "adversarial audio synthesis"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "searching for activation functions"
    - "a noniterative method for reconstruction of phase from stft magnitude"

Connected through hyperedge HE-5 (induced by source paper "conditioning deep generative raw audio models for structured automatic music"):
  with members:
    - "parallel wavenet fast high fidelity speech synthesis"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "deep learning for music"
    - "youtube 8m a large scale video classification benchmark"
    - "deepbach a steerable model for bach chorales generation"
    - "pixel recurrent neural networks"
    - "visualizing and understanding recurrent networks"
    - "polyphonic music generation by modeling temporal dependencies using a rnn dbn"

Connected through hyperedge HE-6 (induced by source paper "ufans u shaped fully parallel acoustic neural structure for statistical parametric speech synthesis with 20x faster"):
  with members:
    - "boosting noise robustness of acoustic model via deep adversarial training"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "opening the black box of deep neural networks via information"
    - "wavenet a generative model for raw audio"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "a tutorial on deep learning for music information retrieval"):
  with members:
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "began boundary equilibrium generative adversarial networks"
    - "the effects of noisy labels on deep convolutional neural networks for music tagging"
    - "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"
    - "revisiting the problem of audio based hit song prediction using convolutional neural networks"
    - "stacked convolutional and recurrent neural networks for music emotion recognition"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "segan speech enhancement generative adversarial network"

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)