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: "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
  Abstract: In this paper we propose a Deep Neural Network (DNN) based Speech Enhancement (SE) system that is designed to maximize an approximation of the Short-Time Objective Intelligibility (STOI) measure. We formalize an approximate-STOI cost function and derive analytical expressions for the gradients required for DNN training and show that these gradients have desirable properties when used together with gradient based optimization techniques. We show through simulation experiments that the proposed SE system achieves large improvements in estimated speech intelligibility, when tested on matched and unmatched natural noise types, at multiple signal-to-noise ratios. Furthermore, we show that the SE system, when trained using an approximate-STOI cost function performs on par with a system trained with a mean square error cost applied to short-time temporal envelopes. Finally, we show that the proposed SE system performs on par with a traditional DNN based Short-Time Spectral Amplitude (STSA) SE system in terms of estimated speech intelligibility. These results are important because they suggest that traditional DNN based STSA SE systems might be optimal in terms of estimated speech intelligibility.

Connected through hyperedge HE-1 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "on the relationship between short time objective intelligibility and short time spectral amplitude mean square error for speech enhancement"
    - "on the variance of the adaptive learning rate and beyond"
    - "a comprehensive study of speech separation spectrogram vs waveform separation"
    - "sdr half baked or well done"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"
    - "using recurrences in time and frequency within u net architecture for speech enhancement"

Connected through hyperedge HE-2 (induced by source paper "deep learning for audio signal processing"):
  with members:
    - "piano genie"
    - "toward domain invariant speech recognition via large scale training"
    - "efficient neural audio synthesis"
    - "deep contextualized word representations"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "supervised speech separation based on deep learning an overview"

Connected through hyperedge HE-3 (induced by source paper "metricgan generative adversarial networks based black box metric scores optimization for speech enhancement"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "learning with learned loss function speech enhancement with quality net to improve perceptual evaluation of speech quality"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "spectral feature mapping with mimic loss for robust speech recognition"
    - "spectral normalization for generative adversarial networks"
    - "quality net an end to end non intrusive speech quality assessment model based on blstm"
    - "noise adaptive speech enhancement using domain adversarial training"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "a supervised speech enhancement approach with residual noise control for voice communication"):
  with members:
    - "on loss functions for supervised monaural time domain speech enhancement"
    - "sdr half baked or well done"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "monaural source enhancement maximizing source to distortion ratio via automatic differentiation"):
  with members:
    - "a wavenet for speech denoising"
    - "wavenet a generative model for raw audio"
    - "automatic differentiation in machine learning a survey"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "on the equivalence between objective intelligibility and mean squared error for deep neural network based speech enhancement"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "supervised speech separation based on deep learning an overview"
    - "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)