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: "unsupervised singing voice conversion"
  Abstract: We present a deep learning method for singing voice conversion. The proposed network is not conditioned on the text or on the notes, and it directly converts the audio of one singer to the voice of another. Training is performed without any form of supervision: no lyrics or any kind of phonetic features, no notes, and no matching samples between singers. The proposed network employs a single CNN encoder for all singers, a single WaveNet decoder, and a classifier that enforces the latent representation to be singer-agnostic. Each singer is represented by one embedding vector, which the decoder is conditioned on. In order to deal with relatively small datasets, we propose a new data augmentation scheme, as well as new training losses and protocols that are based on backtranslation. Our evaluation presents evidence that the conversion produces natural signing voices that are highly recognizable as the target singer.

Connected through hyperedge HE-1 (induced by source paper "mel spectrogram augmentation for sequence to sequence voice conversion"):
  with members:
    - "sequence to sequence acoustic modeling for voice conversion"
    - "sample efficient adaptive text to speech"
    - "tacotron towards end to end speech synthesis"
    - "is the deconvolution layer the same as a convolutional layer"
    - "wavenet a generative model for raw audio"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion"):
  with members:
    - "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"
    - "efficient neural audio synthesis"

Connected through hyperedge HE-3 (induced by source paper "music source separation in the waveform domain"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "sing symbol to instrument neural generator"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "end to end music source separation is it possible in the waveform domain"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "a style based generator architecture for generative adversarial networks"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "denoising auto encoder with recurrent skip connections and residual regression for music source separation"

Connected through hyperedge HE-4 (induced by source paper "learning singing from speech"):
  with members:
    - "data efficient voice cloning for neural singing synthesis"
    - "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 "pitchnet unsupervised singing voice conversion with pitch adversarial network"):
  with members:
    - "a universal music translation network"
    - "wavenet a generative model for raw audio"
    - "adam a method for stochastic optimization"

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.LG(Machine Learning)