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: "adversarial semi supervised audio source separation applied to singing voice extraction"
  Abstract: The state of the art in music source separation employs neural networks trained in a supervised fashion on multi-track databases to estimate the sources from a given mixture. With only few datasets available, often extensive data augmentation is used to combat overfitting. Mixing random tracks, however, can even reduce separation performance as instruments in real music are strongly correlated. The key concept in our approach is that source estimates of an optimal separator should be indistinguishable from real source signals. Based on this idea, we drive the separator towards outputs deemed as realistic by discriminator networks that are trained to tell apart real from separator samples. This way, we can also use unpaired source and mixture recordings without the drawbacks of creating unrealistic music mixtures. Our framework is widely applicable as it does not assume a specific network architecture or number of sources. To our knowledge, this is the first adoption of adversarial training for music source separation. In a prototype experiment for singing voice separation, separation performance increases with our approach compared to purely supervised training.

Connected through hyperedge HE-1 (induced by source paper "source separation with deep generative priors"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "music source separation in the waveform domain"
    - "end to end music source separation is it possible in the waveform domain"
    - "single image reflection separation with perceptual losses"
    - "neural separation of observed and unobserved distributions"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "dynamic routing between capsules"

Connected through hyperedge HE-2 (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"
    - "single channel audio source separation using convolutional denoising autoencoders"

Connected through hyperedge HE-3 (induced by source paper "snore gans improving automatic snore sound classification with synthesized data"):
  with members:
    - "generative adversarial networks an overview"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "learning to fuse music genres with generative adversarial dual learning"
    - "learning audio sequence representations for acoustic event classification"
    - "segan speech enhancement generative adversarial network"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "end to end multimodal emotion recognition using deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "co separating sounds of visual objects"):
  with members:
    - "the sound of motions"
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual event localization in unconstrained videos"
    - "self supervised generation of spatial audio for 360 video"
    - "learning to separate object sounds by watching unlabeled video"
    - "2 5d visual sound"

Connected through hyperedge HE-5 (induced by source paper "demucs deep extractor for music sources with extra unlabeled data remixed"):
  with members:
    - "sing symbol to instrument neural generator"
    - "end to end music source separation is it possible in the waveform domain"
    - "kymatio scattering transforms in python"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "a style based generator architecture for generative adversarial networks"
    - "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"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-6 (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-7 (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-8 (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"
    - "svsgan singing voice separation via generative adversarial network"
    - "a recurrent encoder decoder approach with skip filtering connections for monaural singing voice separation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep karaoke extracting vocals from musical mixtures using a convolutional deep neural network"

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.LG(Machine Learning)