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 "single channel audio source separation using convolutional denoising autoencoders"):
  Abstract: Deep learning techniques have been used recently to tackle the audio source separation problem. In this work, we propose to use deep fully convolutional denoising autoencoders (CDAEs) for monaural audio source separation. We use as many CDAEs as the number of sources to be separated from the mixed signal. Each CDAE is trained to separate one source and treats the other sources as background noise. The main idea is to allow each CDAE to learn suitable spectral-temporal filters and features to its corresponding source. Our experimental results show that CDAEs perform source separation slightly better than the deep feedforward neural networks (FNNs) even with fewer parameters than FNNs.

Includes members:
  Member "convolutional gated recurrent neural network incorporating spatial features for audio tagging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recurrent neural networks an embedded computing perspective")
  Member "deep convolutional neural networks for predominant instrument recognition in polyphonic music":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an attention mechanism for musical instrument recognition")
  Member "a fully convolutional neural network for speech enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "a fully convolutional deep auditory model for musical chord recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling music modality with a key class invariant pitch chroma cnn")
  Member "a neural network alternative to non negative audio models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep audio prior")
  Member "discriminative enhancement for single channel audio source separation using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an overview of lead and accompaniment separation in music")
  Member "convolutional recurrent neural networks for music classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "music2dance dancenet for music driven dance generation")
  Member "source separation using regularized nmf with mmse estimates under gmm priors with online learning for the uncertainties":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi resolution fully convolutional neural networks for monaural audio source separation")

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)