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: "unsupervised audio source separation via spectrum energy preserved wasserstein learning"
  Abstract: Separating audio mixtures into individual tracks has been a long standing challenging task. We introduce a novel unsupervised audio source separation approach based on deep adversarial learning. Specifically, our loss function adopts the Wasserstein distance which directly measures the distribution distance between the separated sources and the real sources for each individual source. Moreover, a global regularization term is added to fulfill the spectrum energy preservation property regarding separation. Unlike state-of-the-art unsupervised models which often involve deliberately devised constraints or careful model selection, our approach need little prior model specification on the data, and can be straightforwardly learned in an end-to-end fashion. We show that the proposed method performs competitively on public benchmark against state-of-the-art unsupervised methods.

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"
    - "multi scale multi band densenets for audio source separation"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "single channel audio source separation using convolutional denoising autoencoders"

Connected through hyperedge HE-2 (induced by source paper "finding strength in weakness learning to separate sounds with weak supervision"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "audio source separation using variational autoencoders and weak class supervision"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "cutting music source separation some slakh a dataset to study the impact of training data quality and quantity"
    - "universal sound separation"
    - "co separating sounds of visual objects"
    - "sdr half baked or well done"

Connected through hyperedge HE-3 (induced by source paper "training generative adversarial networks from incomplete observations using factorised discriminators"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "gain missing data imputation using generative adversarial nets"
    - "spectral normalization for generative adversarial networks"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "a style based generator architecture for generative adversarial networks"
    - "learning image to image translation using paired and unpaired training samples"
    - "triangle generative adversarial networks"

Connected through hyperedge HE-4 (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"
    - "learning latent representations for speech generation and transformation"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "adam a method for stochastic optimization"

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"
    - "generative adversarial source separation"
    - "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"

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)