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: "generative adversarial source separation"
  Abstract: Generative source separation methods such as non-negative matrix factorization (NMF) or auto-encoders, rely on the assumption of an output probability density. Generative Adversarial Networks (GANs) can learn data distributions without needing a parametric assumption on the output density. We show on a speech source separation experiment that, a multi-layer perceptron trained with a Wasserstein-GAN formulation outperforms NMF, auto-encoders trained with maximum likelihood, and variational auto-encoders in terms of source to distortion ratio.

Connected through hyperedge HE-1 (induced by source paper "deep learning for audio signal processing"):
  with members:
    - "piano genie"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "universal sound separation"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "fixup initialization residual learning without normalization"
    - "sdr half baked or well done"
    - "speaker independent speech separation with deep attractor network"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "differentiable consistency constraints for improved deep speech enhancement"
    - "exploring tradeoffs in models for low latency speech enhancement"

Connected through hyperedge HE-3 (induced by source paper "cass cross adversarial source separation via autoencoder"):
  with members:
    - "an introduction to image synthesis with generative adversarial nets"
    - "svsgan singing voice separation via generative adversarial network"
    - "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-4 (induced by source paper "neural separation of observed and unobserved distributions"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "nam non adversarial unsupervised domain mapping"
    - "unsupervised learning of disentangled representations from video"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "seeing through noise visually driven speaker separation and enhancement"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "monaural singing voice separation with skip filtering connections and recurrent inference of time frequency mask"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

Connected through hyperedge HE-5 (induced by source paper "semi supervised monaural singing voice separation with a masking network trained on synthetic mixtures"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "mad twinnet masker denoiser architecture with twin networks for monaural sound source separation"
    - "neural separation of observed and unobserved distributions"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a theory of output side unsupervised domain adaptation"
    - "monaural singing voice separation with skip filtering connections and recurrent inference of time frequency mask"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

Connected through hyperedge HE-6 (induced by source paper "weak label supervision for monaural source separation using non negative denoising variational autoencoders"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "supervised speech separation based on deep learning an overview"
    - "unsupervised audio source separation via spectrum energy preserved wasserstein learning"
    - "learning latent representations for speech generation and transformation"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "structured dropout for weak label and multi instance learning and its application to score informed source separation"
    - "a neural network alternative to non negative audio models"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"

Connected through hyperedge HE-7 (induced by source paper "unsupervised audio source separation via spectrum energy preserved wasserstein learning"):
  with members:
    - "adversarial neural machine translation"
    - "svsgan singing voice separation via generative adversarial network"
    - "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)