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 noniterative method for reconstruction of phase from stft magnitude"
  Abstract: A noniterative method for the reconstruction of the short-time fourier transform (STFT) phase from the magnitude is presented. The method is based on the direct relationship between the partial derivatives of the phase and the logarithm of the magnitude of the un-sampled STFT with respect to the Gaussian window. Although the theory holds in the continuous setting only, the experiments show that the algorithm performs well even in the discretized setting (discrete Gabor transform) with low redundancy using the sampled Gaussian window, the truncated Gaussian window and even other compactly supported windows such as the Hann window. Due to the noniterative nature, the algorithm is very fast and it is suitable for long audio signals. Moreover, solutions of iterative phase reconstruction algorithms can be improved considerably by initializing them with the phase estimate provided by the present algorithm. We present an extensive comparison with the state-of-the-art algorithms in a reproducible manner.

Connected through hyperedge HE-1 (induced by source paper "adversarial generation of time frequency features with application in audio synthesis"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "a context encoder for audio inpainting"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "large scale gan training for high fidelity natural image synthesis"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "adversarial audio synthesis"
    - "improving dnn based music source separation using phase features"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "fast spectrogram inversion using multi head convolutional neural networks"):
  with members:
    - "audio style transfer"
    - "neural voice cloning with a few samples"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "deep voice real time neural text to speech"
    - "deep voice 3 2000 speaker neural text to speech"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-3 (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"
    - "phase reconstruction from amplitude spectrograms based on von mises distribution deep neural network"
    - "adversarial audio synthesis"
    - "a neural parametric singing synthesizer"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "searching for activation functions"

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)