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: "single channel audio source separation using convolutional denoising autoencoders"
  Abstract: Deep learning techniques have been used recently to tackle the audio source separation problem. In this work, we propose to use deep fully convolutional denoising autoencoders (CDAEs) for monaural audio source separation. We use as many CDAEs as the number of sources to be separated from the mixed signal. Each CDAE is trained to separate one source and treats the other sources as background noise. The main idea is to allow each CDAE to learn suitable spectral-temporal filters and features to its corresponding source. Our experimental results show that CDAEs perform source separation slightly better than the deep feedforward neural networks (FNNs) even with fewer parameters than FNNs.

Connected through hyperedge HE-1 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis"):
  with members:
    - "audio source separation using variational autoencoders and weak class supervision"
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "universal sound separation"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "panns large scale pretrained audio neural networks for audio pattern recognition"
    - "unsupervised audio source separation via spectrum energy preserved wasserstein learning"
    - "multi scale multi band densenets for audio source separation"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

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 "audio source separation using variational autoencoders and weak class supervision"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "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"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (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"
    - "generative adversarial source separation"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "deep residual learning for image recognition"
    - "source separation and higher order causal analysis of meg and eeg"

Connected through hyperedge HE-5 (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"
    - "generative adversarial source separation"
    - "learning latent representations for speech generation and transformation"
    - "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-6 (induced by source paper "using recurrences in time and frequency within u net architecture for speech enhancement"):
  with members:
    - "convolutional recurrent neural networks for speech enhancement"
    - "deep learning for environmentally robust speech recognition an overview of recent developments"
    - "supervised speech separation based on deep learning an overview"
    - "a fully convolutional neural network for speech enhancement"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"
    - "u net convolutional networks for biomedical image segmentation"
    - "renet a recurrent neural network based alternative to convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "raw multi channel audio source separation using multi resolution convolutional auto encoders"):
  with members:
    - "a tutorial on deep learning for music information retrieval"
    - "does phase matter for monaural source separation"
    - "time frequency trade offs for audio source separation with binary masks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "visual speech enhancement"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual scene analysis with self supervised multisensory features"
    - "improved speech reconstruction from silent video"
    - "vid2speech speech reconstruction from silent video"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "segan speech enhancement generative adversarial network"
    - "seeing through noise visually driven speaker separation and enhancement"

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)