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 "singing voice separation a study on training data"):
  Abstract: In the recent years, singing voice separation systems showed increased performance due to the use of supervised training. The design of training datasets is known as a crucial factor in the performance of such systems. We investigate on how the characteristics of the training dataset impacts the separation performances of state-of-the-art singing voice separation algorithms. We show that the separation quality and diversity are two important and complementary assets of a good training dataset. We also provide insights on possible transforms to perform data augmentation for this task.

Includes members:
  Member "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time domain audio source separation based on wave u net combined with discrete wavelet transform")
  Member "wave u net a multi scale neural network for end to end audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "source separation with deep generative priors")
  Member "multi scale multi band densenets for audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")
  Member "svsgan singing voice separation via generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial generation of time frequency features with application in audio synthesis")
  Member "a recurrent encoder decoder approach with skip filtering connections for monaural singing voice separation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "investigating kernel shapes and skip connections for deep learning based harmonic percussive separation")
  Member "adversarial semi supervised audio source separation applied to singing voice extraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "source separation with deep generative priors")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deep karaoke extracting vocals from musical mixtures using a convolutional deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the sound of motions")

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)