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 "wavelet speech enhancement based on nonnegative matrix factorization"):
  Abstract: For the state-of-the-art speech enhancement (SE) techniques, a spectrogram is usually preferred than the respective time-domain raw data, since it reveals more compact presentation together with conspicuous temporal information over a long time span. However, two problems can cause distortions in the conventional nonnegative matrix factorization (NMF)-based SE algorithms. One is related to the overlap-and-add operation used in the short-time Fourier transform (STFT)-based signal reconstruction, and the other is concerned with directly using the phase of the noisy speech as that of the enhanced speech in signal reconstruction. These two problems can cause information loss or discontinuity when comparing the clean signal with the reconstructed signal. To solve these two problems, we propose a novel SE method that adopts discrete wavelet packet transform (DWPT) and NMF. In brief, the DWPT is first applied to split a time-domain speech signal into a series of subband signals. Then, we exploit NMF to highlight the speech component for each subband. These enhanced subband signals are joined together via the inverse DWPT to reconstruct a noise-reduced signal in time domain. We evaluate the proposed DWPT-NMF-based SE method on the Mandarin hearing in noise test (MHINT) task. Experimental results show that this new method effectively enhances speech quality and intelligibility and outperforms the conventional STFT-NMF-based SE system.

Includes members:
  Member "supervised and unsupervised speech enhancement using nonnegative matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "audio visual speech enhancement using conditional variational auto encoder")
  Member "an investigation of wavelet packet transform for spectrum estimation":
    also connected through other hyperedges:
      (none)

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)