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: "model based stft phase recovery for audio source separation"
  Abstract: For audio source separation applications, it is common to estimate the magnitude of the short-time Fourier transform (STFT) of each source. In order to further synthesizing time-domain signals, it is necessary to recover the phase of the corresponding complex-valued STFT. Most authors in this field choose a Wiener-like filtering approach which boils down to using the phase of the original mixture. In this paper, a different standpoint is adopted. Many music events are partially composed of slowly varying sinusoids and the STFT phase increment over time of those frequency components takes a specific form. This allows phase recovery by an unwrapping technique once a short-term frequency estimate has been obtained. Herein, a novel iterative source separation procedure is proposed which builds upon these results. It consists in minimizing the mixing error by means of the auxiliary function method. This procedure is initialized by exploiting the unwrapping technique in order to generate estimates that benefit from a temporal continuity property. Experiments conducted on realistic music pieces show that, given accurate magnitude estimates, this procedure outperforms the state-of-the-art consistent Wiener filter.

Connected through hyperedge HE-1 (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"
    - "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"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-2 (induced by source paper "online spectrogram inversion for low latency audio source separation"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "low latency deep clustering for speech 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"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "differentiable consistency constraints for improved deep speech enhancement"
    - "supervised speech separation based on deep learning an overview"

Connected through hyperedge HE-3 (induced by source paper "harmonic percussive source separation with deep neural networks and phase recovery"):
  with members:
    - "mad twinnet masker denoiser architecture with twin networks for monaural sound source separation"
    - "multi scale multi band densenets for audio source separation"
    - "a recurrent encoder decoder approach with skip filtering connections for monaural singing voice separation"
    - "monaural singing voice separation with skip filtering connections and recurrent inference of time frequency mask"
    - "memory augmented neural networks with wormhole connections"
    - "twin networks matching the future for sequence generation"
    - "improving neural language models with a continuous cache"
    - "topicrnn a recurrent neural network with long range semantic dependency"

Connected through hyperedge HE-4 (induced by source paper "complex isnmf a phase aware model for monaural audio source separation"):
  with members:
    - "multi scale multi band densenets for audio source separation"
    - "phase reconstruction of spectrograms with linear unwrapping application to audio signal restoration"
    - "complex nmf under phase constraints based on signal modeling application to audio source separation"
    - "phase recovery in nmf for audio source separation an insightful benchmark"

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)