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: "supervised and unsupervised speech enhancement using nonnegative matrix factorization"
  Abstract: Reducing the interference noise in a monaural noisy speech signal has been a challenging task for many years. Compared to traditional unsupervised speech enhancement methods, e.g., Wiener filtering, supervised approaches, such as algorithms based on hidden Markov models (HMM), lead to higher-quality enhanced speech signals. However, the main practical difficulty of these approaches is that for each noise type a model is required to be trained a priori. In this paper, we investigate a new class of supervised speech denoising algorithms using nonnegative matrix factorization (NMF). We propose a novel speech enhancement method that is based on a Bayesian formulation of NMF (BNMF). To circumvent the mismatch problem between the training and testing stages, we propose two solutions. First, we use an HMM in combination with BNMF (BNMF-HMM) to derive a minimum mean square error (MMSE) estimator for the speech signal with no information about the underlying noise type. Second, we suggest a scheme to learn the required noise BNMF model online, which is then used to develop an unsupervised speech enhancement system. Extensive experiments are carried out to investigate the performance of the proposed methods under different conditions. Moreover, we compare the performance of the developed algorithms with state-of-the-art speech enhancement schemes using various objective measures. Our simulations show that the proposed BNMF-based methods outperform the competing algorithms substantially.

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 "single channel speech enhancement using temporal convolutional recurrent neural networks"):
  with members:
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "end to end speech recognition from the raw waveform"
    - "supervised speech separation based on deep learning an overview"
    - "a wavenet for speech denoising"
    - "wavenet a generative model for raw audio"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "on cross corpus generalization of deep learning based speech enhancement"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "voxceleb2 deep speaker recognition"
    - "supervised speech separation based on deep learning an overview"
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "on the effectiveness of low rank matrix factorization for lstm model compression"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "deep contextualized word representations"
    - "end to end neural coreference resolution"
    - "on the state of the art of evaluation in neural language models"
    - "learned in translation contextualized word vectors"
    - "learning intrinsic sparse structures within long short term memory"
    - "tensor train recurrent neural networks for video classification"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-5 (induced by source paper "deep speech enhancement for reverberated and noisy signals using wide residual networks"):
  with members:
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "multichannel speech enhancement by raw waveform mapping using fully convolutional networks"):
  with members:
    - "dilated fcn listening longer to hear better"
    - "adversarial audio synthesis"
    - "speech dereverberation based on integrated deep and ensemble learning algorithm"
    - "raw multi channel audio source separation using multi resolution convolutional auto encoders"
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"
    - "multi resolution fully convolutional neural networks for monaural audio source separation"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-7 (induced by source paper "modeling the comb filter effect and interaural coherence for binaural source separation"):
  with members:
    - "acoustic reflector localization novel image source reversion and direct localization methods"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "acoustic space learning for sound source separation and localization on binaural manifolds"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "raking the cocktail party"
    - "audio surveillance a systematic review"

Connected through hyperedge HE-8 (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"
    - "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"

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)