human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "demucs deep extractor for music sources with extra unlabeled data remixed"):
  Abstract: We study the problem of source separation for music using deep learning with four known sources: drums, bass, vocals and other accompaniments. State-of-the-art approaches predict soft masks over mixture spectrograms while methods working on the waveform are lagging behind as measured on the standard MusDB benchmark. Our contribution is two fold. (i) We introduce a simple convolutional and recurrent model that outperforms the state-of-the-art model on waveforms, that is, Wave-U-Net, by 1.6 points of SDR (signal to distortion ratio). (ii) We propose a new scheme to leverage unlabeled music. We train a first model to extract parts with at least one source silent in unlabeled tracks, for instance without bass. We remix this extract with a bass line taken from the supervised dataset to form a new weakly supervised training example. Combining our architecture and scheme, we show that waveform methods can play in the same ballpark as spectrogram ones.

Includes members:
  Member "sing symbol to instrument neural generator":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ddsp differentiable digital signal processing")
  Member "end to end music source separation is it possible in the waveform domain":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "source separation with deep generative priors")
  Member "kymatio scattering transforms in python":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "biologically inspired architectures for sample efficient deep reinforcement learning")
  Member "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "time domain audio source separation based on wave u net combined with discrete wavelet transform")
  Member "a style based generator architecture for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "wave u net a multi scale neural network for end to end audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "source separation with deep generative priors")
  Member "denoising auto encoder with recurrent skip connections and residual regression for music source separation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "addressing the confounds of accompaniments in singer identification")
  Member "progressive growing of gans for improved quality stability and variation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

Question: Please tell me which class the source paper 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)