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 "speaker independence of neural vocoders and their effect on parametric resynthesis speech enhancement"):
  Abstract: Traditional speech enhancement systems produce speech with compromised quality. Here we propose to use the high quality speech generation capability of neural vocoders for better quality speech enhancement. We term this parametric resynthesis (PR). In previous work, we showed that PR systems generate high quality speech for a single speaker using two neural vocoders, WaveNet and WaveGlow. Both these vocoders are traditionally speaker dependent. Here we first show that when trained on data from enough speakers, these vocoders can generate speech from unseen speakers, both male and female, with similar quality as seen speakers in training. Next using these two vocoders and a new vocoder LPCNet, we evaluate the noise reduction quality of PR on unseen speakers and show that objective signal and overall quality is higher than the state-of-the-art speech enhancement systems Wave-U-Net, Wavenet-denoise, and SEGAN. Moreover, in subjective quality, multiple-speaker PR out-performs the oracle Wiener mask.

Includes members:
  Member "parametric resynthesis with neural vocoders":
    also connected through other hyperedges:
      (none)
  Member "waveglow a flow based generative network for speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "videoflow a conditional flow based model for stochastic video generation")
  Member "efficient neural audio synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "what is the state of neural network pruning")
  Member "wave u net a multi scale neural network for end to end audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "source separation with deep generative priors")
  Member "improved speech enhancement with the wave u net":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parametric resynthesis with neural vocoders")
  Member "segan speech enhancement generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "deep voice real time neural text to speech":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck")
  Member "a wavenet for speech denoising":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bayesian neural networks with maximum mean discrepancy regularization")

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)