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: "end to end music source separation is it possible in the waveform domain"
  Abstract: Most of the currently successful source separation techniques use the magnitude spectrogram as input, and are therefore by default omitting part of the signal: the phase. To avoid omitting potentially useful information, we study the viability of using end-to-end models for music source separation --- which take into account all the information available in the raw audio signal, including the phase. Although during the last decades end-to-end music source separation has been considered almost unattainable, our results confirm that waveform-based models can perform similarly (if not better) than a spectrogram-based deep learning model. Namely: a Wavenet-based model we propose and Wave-U-Net can outperform DeepConvSep, a recent spectrogram-based deep learning model.

Connected through hyperedge HE-1 (induced by source paper "source separation with deep generative priors"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "music source separation in the waveform domain"
    - "single image reflection separation with perceptual losses"
    - "neural separation of observed and unobserved distributions"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "dynamic routing between capsules"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

Connected through hyperedge HE-2 (induced by source paper "time domain audio source separation based on wave u net combined with discrete wavelet transform"):
  with members:
    - "making convolutional networks shift invariant again"
    - "universal sound separation"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "end to end sound source separation conditioned on instrument labels"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "end to end source separation with adaptive front ends"
    - "multi scale multi band densenets for audio source separation"
    - "density estimation using real nvp"

Connected through hyperedge HE-3 (induced by source paper "exploiting synchronized lyrics and vocal features for music emotion detection"):
  with members:
    - "convolutional attention networks for multimodal emotion recognition from speech and text data"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "music mood detection based on audio and lyrics with deep neural net"
    - "a tutorial on deep learning for music information retrieval"
    - "attention is all you need"
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"

Connected through hyperedge HE-4 (induced by source paper "demucs deep extractor for music sources with extra unlabeled data remixed"):
  with members:
    - "sing symbol to instrument neural generator"
    - "kymatio scattering transforms in python"
    - "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"
    - "progressive growing of gans for improved quality stability and variation"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-5 (induced by source paper "music source separation in the waveform domain"):
  with members:
    - "unsupervised singing voice conversion"
    - "fixup initialization residual learning without normalization"
    - "sing symbol to instrument neural generator"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "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"

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)