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: "svsgan singing voice separation via generative adversarial network"
  Abstract: Separating two sources from an audio mixture is an important task with many applications. It is a challenging problem since only one signal channel is available for analysis. In this paper, we propose a novel framework for singing voice separation using the generative adversarial network (GAN) with a time-frequency masking function. The mixture spectra is considered to be a distribution and is mapped to the clean spectra which is also considered a distribtution. The approximation of distributions between mixture spectra and clean spectra is performed during the adversarial training process. In contrast with current deep learning approaches for source separation, the parameters of the proposed framework are first initialized in a supervised setting and then optimized by the training procedure of GAN in an unsupervised setting. Experimental results on three datasets (MIR-1K, iKala and DSD100) show that performance can be improved by the proposed framework consisting of conventional networks.

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 "probabilistic forecasting of sensory data with generative adversarial networks forgan"):
  with members:
    - "phaselink a deep learning approach to seismic phase association"
    - "predicting inpatient discharge prioritization with electronic health records"
    - "deepdownscale a deep learning strategy for high resolution weather forecast"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "voice impersonation using generative adversarial networks"
    - "cred a deep residual network of convolutional and recurrent units for earthquake signal detection"
    - "adversarial learning for neural dialogue generation"
    - "model free renewable scenario generation using generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "classical music generation in distinct dastgahs with alimnet acgan"):
  with members:
    - "instrument independent dastgah recognition of iranian classical music using azarnet"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "a report on sound event detection with different binaural features"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "tac gan text conditioned auxiliary classifier generative adversarial network"
    - "neural discrete representation learning"
    - "samplernn an unconditional end to end neural audio generation model"

Connected through hyperedge HE-4 (induced by source paper "singing voice separation a study on training data"):
  with members:
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "wave u net a multi scale neural network for end to end audio 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"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep karaoke extracting vocals from musical mixtures using a convolutional deep neural network"

Connected through hyperedge HE-5 (induced by source paper "cass cross adversarial source separation via autoencoder"):
  with members:
    - "an introduction to image synthesis with generative adversarial nets"
    - "generative adversarial source separation"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "deep residual learning for image recognition"
    - "source separation and higher order causal analysis of meg and eeg"

Connected through hyperedge HE-6 (induced by source paper "singing voice separation using a deep convolutional neural network trained by ideal binary mask and cross entropy"):
  with members:
    - "jointly detecting and separating singing voice a multi task approach"
    - "an overview of lead and accompaniment separation in music"
    - "a functional taxonomy of music generation systems"
    - "exploiting saliency for object segmentation from image level labels"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "densely connected convolutional networks"
    - "singing voice separation and vocal f0 estimation based on mutual combination of robust principal component analysis and subharmonic summation"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"

Connected through hyperedge HE-7 (induced by source paper "spectrogram channels u net a source separation model viewing each channel as the spectrogram of each source"):
  with members:
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "denoising auto encoder with recurrent skip connections and residual regression for music source separation"
    - "segan speech enhancement generative adversarial network"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "unsupervised audio source separation via spectrum energy preserved wasserstein learning"):
  with members:
    - "adversarial neural machine translation"
    - "generative adversarial source separation"
    - "improved training of wasserstein gans"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

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)