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 "audio visual speech enhancement using conditional variational auto encoder"):
  Abstract: Variational auto-encoders (VAEs) are deep generative latent variable models that can be used for learning the distribution of complex data. VAEs have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. One advantage of this generative approach is that it does not require pairs of clean and noisy speech signals at training. In this paper, we propose audio-visual variants of VAEs for single-channel and speaker-independent speech enhancement. We develop a conditional VAE (CVAE) where the audio speech generative process is conditioned on visual information of the lip region. At test time, the audio-visual speech generative model is combined with a noise model based on nonnegative matrix factorization, and speech enhancement relies on a Monte Carlo expectation-maximization algorithm. Experiments are conducted with the recently published NTCD-TIMIT dataset. The results confirm that the proposed audio-visual CVAE effectively fuse audio and visual information, and it improves the speech enhancement performance compared with the audio-only VAE model, especially when the speech signal is highly corrupted by noise. We also show that the proposed unsupervised audio-visual speech enhancement approach outperforms a state-of-the-art supervised deep learning method.

Includes members:
  Member "semi supervised multichannel speech enhancement with variational autoencoders and non negative matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a statistically principled and computationally efficient approach to speech enhancement using variational autoencoders")
  Member "a statistically principled and computationally efficient approach to speech enhancement using variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a recurrent variational autoencoder for speech enhancement")
  Member "a deep generative model of speech complex spectrograms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a statistically principled and computationally efficient approach to speech enhancement using variational autoencoders")
  Member "speech enhancement with variational autoencoders and alpha stable distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust speaker recognition using speech enhancement and attention model")
  Member "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep speech enhancement for reverberated and noisy signals using wide residual networks")
  Member "the conversation deep audio visual speech enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "listen to look action recognition by previewing audio")
  Member "end to end audiovisual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lipreading using temporal convolutional networks")
  Member "fast mvae joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a statistically principled and computationally efficient approach to speech enhancement using variational autoencoders")

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)