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: "a variance modeling framework based on variational autoencoders for speech enhancement"
  Abstract: In this paper we address the problem of enhancing speech signals in noisy mixtures using a source separation approach. We explore the use of neural networks as an alternative to a popular speech variance model based on supervised non-negative matrix factorization (NMF). More precisely, we use a variational autoencoder as a speaker-independent supervised generative speech model, highlighting the conceptual similarities that this approach shares with its NMF-based counterpart. In order to be free of generalization issues regarding the noisy recording environments, we follow the approach of having a supervised model only for the target speech signal, the noise model being based on unsupervised NMF. We develop a Monte Carlo expectation-maximization algorithm for inferring the latent variables in the variational autoencoder and estimating the unsupervised model parameters. Experiments show that the proposed method outperforms a semi-supervised NMF baseline and a state-of-the-art fully supervised deep learning approach.

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"
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a deep generative model of speech complex spectrograms"):
  with members:
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "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"
    - "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-3 (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"
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "fast mvae joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier"
    - "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-4 (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"
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "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-5 (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"
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "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-6 (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"
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "supervised speech separation based on deep learning an overview"
    - "adam a method for stochastic optimization"
    - "supervised and unsupervised speech enhancement using nonnegative matrix factorization"

Connected through hyperedge HE-7 (induced by source paper "unsupervised speech enhancement based on multichannel nmf informed beamforming for noise robust automatic speech recognition"):
  with members:
    - "fast multichannel source separation based on jointly diagonalizable spatial covariance matrices"
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "rank 1 constrained multichannel wiener filter for speech recognition in noisy environments"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "fast multichannel source separation based on jointly diagonalizable spatial covariance matrices"):
  with members:
    - "semi supervised multichannel speech enhancement with variational autoencoders and non negative matrix factorization"
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "fastfca a joint diagonalization based fast algorithm for audio source separation using a full rank spatial covariance model"
    - "fastfca as joint diagonalization based acceleration of full rank spatial covariance analysis for separating any number of sources"

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)