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: "phase reconstruction from amplitude spectrograms based on von mises distribution deep neural network"
  Abstract: This paper presents a deep neural network (DNN)-based phase reconstruction from amplitude spectrograms. In audio signal and speech processing, the amplitude spectrogram is often used for processing, and the corresponding phase spectrogram is reconstructed from the amplitude spectrogram on the basis of the Griffin-Lim method. However, the Griffin-Lim method causes unnatural artifacts in synthetic speech. Addressing this problem, we introduce the von-Mises-distribution DNN for phase reconstruction. The DNN is a generative model having the von Mises distribution that can model distributions of a periodic variable such as a phase, and the model parameters of the DNN are estimated on the basis of the maximum likelihood criterion. Furthermore, we propose a group-delay loss for DNN training to make the predicted group delay close to a natural group delay. The experimental results demonstrate that 1) the trained DNN can predict group delay accurately more than phases themselves, and 2) our phase reconstruction methods achieve better speech quality than the conventional Griffin-Lim method.

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"
    - "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-2 (induced by source paper "deep griffin lim iteration"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "the little engine that could regularization by denoising red"
    - "language modeling with gated convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "phasen a phase and harmonics aware speech enhancement network"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"

Connected through hyperedge HE-4 (induced by source paper "a context encoder for audio inpainting"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "introducing spain sparse audion inpainter"
    - "adversarial audio synthesis"
    - "a neural parametric singing synthesizer"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "searching for activation functions"
    - "a noniterative method for reconstruction of phase from stft magnitude"

Connected through hyperedge HE-5 (induced by source paper "deep learning based phase reconstruction for speaker separation a trigonometric perspective"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "the conversation deep audio visual speech enhancement"
    - "clarinet parallel wave generation in end to end text to speech"
    - "speaker independent speech separation with deep attractor network"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "improving dnn based music source separation using phase features"
    - "supervised speech separation based on deep learning an overview"

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)