human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
  Abstract: This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech. A standard approach to speech enhancement is to train a deep neural network (DNN) to take noisy speech as input and output clean speech. Although this supervised approach requires a very large amount of pair data for training, it is not robust against unknown environments. Another approach is to use non-negative matrix factorization (NMF) based on basis spectra trained on clean speech in advance and those adapted to noise on the fly. This semi-supervised approach, however, causes considerable signal distortion in enhanced speech due to the unrealistic assumption that speech spectrograms are linear combinations of the basis spectra. Replacing the poor linear generative model of clean speech in NMF with a VAE—a powerful nonlinear deep generative model—trained on clean speech, we formulate a unified probabilistic generative model of noisy speech. Given noisy speech as observed data, we can sample clean speech from its posterior distribution. The proposed method outperformed the conventional DNN-based method in unseen noisy environments.

Connected through hyperedge HE-1 (induced by source paper "audio visual speech enhancement using conditional variational auto encoder"):
  with members:
    - "semi supervised multichannel speech enhancement with variational autoencoders and non negative matrix factorization"
    - "a statistically principled and computationally efficient approach to speech enhancement using variational autoencoders"
    - "a deep generative model of speech complex spectrograms"
    - "speech enhancement with variational autoencoders and alpha stable distributions"
    - "the conversation deep audio visual speech enhancement"
    - "end to end audiovisual speech recognition"
    - "fast mvae joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier"
    - "dnn driven speaker independent audio visual mask estimation for speech separation"

Connected through hyperedge HE-2 (induced by source paper "deep speech enhancement for reverberated and noisy signals using wide residual networks"):
  with members:
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "convolutional recurrent neural networks for speech enhancement"
    - "segan speech enhancement generative adversarial network"
    - "decoupled weight decay regularization"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "a fully convolutional neural network for speech enhancement"
    - "wide residual networks"
    - "musan a music speech and noise corpus"

Connected through hyperedge HE-3 (induced by source paper "a deep generative model of speech complex spectrograms"):
  with members:
    - "phase reconstruction from amplitude spectrograms based on von mises distribution deep neural network"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a variance modeling framework based on variational autoencoders for speech enhancement"
    - "language modeling with gated convolutional networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "model based stft phase recovery for audio source separation"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-4 (induced by source paper "a statistically principled and computationally efficient approach to speech enhancement using variational autoencoders"):
  with members:
    - "semi supervised multichannel speech enhancement with variational autoencoders and non negative matrix factorization"
    - "a deep generative model of speech complex spectrograms"
    - "speech enhancement with variational autoencoders and alpha stable distributions"
    - "fast mvae joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier"
    - "a variance modeling framework based on variational autoencoders for speech enhancement"
    - "an overview of gradient descent optimization algorithms"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "a recurrent variational autoencoder for speech enhancement"):
  with members:
    - "semi supervised multichannel speech enhancement with variational autoencoders and non negative matrix factorization"
    - "a statistically principled and computationally efficient approach to speech enhancement using variational autoencoders"
    - "speech enhancement with variational autoencoders and alpha stable distributions"
    - "a variance modeling framework based on variational autoencoders for speech enhancement"
    - "supervised speech separation based on deep learning an overview"
    - "a recurrent latent variable model for sequential data"
    - "adam a method for stochastic optimization"
    - "supervised and unsupervised speech enhancement using nonnegative matrix factorization"

Connected through hyperedge HE-6 (induced by source paper "semi supervised multichannel speech enhancement with variational autoencoders and non negative matrix factorization"):
  with members:
    - "speech enhancement with variational autoencoders and alpha stable distributions"
    - "a variance modeling framework based on variational autoencoders for speech enhancement"
    - "supervised speech separation based on deep learning an overview"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "end to end multi task denoising for joint sdr and pesq optimization"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "sdr half baked or well done"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"

Connected through hyperedge HE-8 (induced by source paper "speech enhancement with variational autoencoders and alpha stable distributions"):
  with members:
    - "semi supervised multichannel speech enhancement with variational autoencoders and non negative matrix factorization"
    - "a variance modeling framework based on variational autoencoders for speech enhancement"
    - "supervised speech separation based on deep learning an overview"
    - "adam a method for stochastic optimization"
    - "supervised and unsupervised speech enhancement using nonnegative matrix factorization"

Question: Please tell me which class the center node 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)