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 "denoising auto encoder with recurrent skip connections and residual regression for music source separation"):
  Abstract: Convolutional neural networks with skip connections have shown good performance in music source separation. In this work, we propose a denoising Auto-encoder with Recurrent skip Connections (ARC). We use 1D convolution along the temporal axis of the time-frequency feature map in all layers of the fully-convolutional network. The use of 1D convolution makes it possible to apply recurrent layers to the intermediate outputs of the convolution layers. In addition, we also propose an enhancement network and a residual regression method to further improve the separation result. The recurrent skip connections, the enhancement module, and the residual regression all improve the separation quality. The ARC model with residual regression achieves 5.74 siganl-to-distoration ratio (SDR) in vocals with MUSDB in SiSEC 2018. We also evaluate the ARC model alone on the older dataset DSD100 (used in SiSEC 2016) and it achieves 5.91 SDR in vocals.

Includes members:
  Member "frame level instrument recognition by timbre and pitch":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning multi instrument classification with partial labels")
  Member "an overview of lead and accompaniment separation in music":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "addressing the confounds of accompaniments in singer identification")
  Member "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time domain audio source separation based on wave u net combined with discrete wavelet transform")
  Member "singing style transfer using cycle consistent boundary equilibrium generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "performancenet score to audio music generation with multi band convolutional residual network")
  Member "mad twinnet masker denoiser architecture with twin networks for monaural sound source separation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semi supervised monaural singing voice separation with a masking network trained on synthetic mixtures")
  Member "vocal melody extraction using patch based cnn":
    also connected through other hyperedges:
      (none)
  Member "music source separation using stacked hourglass networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "audio query based music source separation")
  Member "transcribing lyrics from commercial song audio the first step towards singing content processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "end to end lyrics alignment for polyphonic music using an audio to character recognition model")

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)