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: "monaural singing voice separation with skip filtering connections and recurrent inference of time frequency mask"
  Abstract: Singing voice separation based on deep learning relies on the usage of time-frequency masking. In many cases the masking process is not a learnable function or is not encapsulated into the deep learning optimization. Consequently, most of the existing methods rely on a post processing step using the generalized Wiener filtering. This work proposes a method that learns and optimizes (during training) a source-dependent mask and does not need the aforementioned post processing step. We introduce a recurrent inference algorithm, a sparse transformation step to improve the mask generation process, and a learned denoising filter. Obtained results show an increase of 0.49 dB for the signal to distortion ratio and 0.30 dB for the signal to interference ratio, compared to previous state-of-the-art approaches for monaural singing voice separation.

Connected through hyperedge HE-1 (induced by source paper "dilated convolution with dilated gru for music source separation"):
  with members:
    - "an overview of lead and accompaniment separation in music"
    - "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"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "feudal networks for hierarchical reinforcement learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "multi scale multi band densenets for audio source separation"

Connected through hyperedge HE-2 (induced by source paper "interleaved multitask learning for audio source separation with independent databases"):
  with members:
    - "jointly detecting and separating singing voice a multi task approach"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "music source separation using stacked hourglass networks"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "multi scale multi band densenets for audio source separation"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "overcoming catastrophic forgetting in neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "an overview of lead and accompaniment separation in music"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "multi scale multi band densenets for audio source separation"
    - "a recurrent encoder decoder approach with skip filtering connections for monaural singing voice separation"
    - "single channel multi speaker separation using deep clustering"
    - "singing voice separation and vocal f0 estimation based on mutual combination of robust principal component analysis and subharmonic summation"
    - "discriminative enhancement for single channel audio source separation using deep neural networks"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"
    - "deep clustering discriminative embeddings for segmentation and separation"

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"
    - "generative adversarial source separation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "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"
    - "generative adversarial source separation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a theory of output side unsupervised domain adaptation"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

Connected through hyperedge HE-6 (induced by source paper "mad twinnet masker denoiser architecture with twin networks for monaural sound source separation"):
  with members:
    - "multi scale multi band densenets for audio source separation"
    - "a recurrent encoder decoder approach with skip filtering connections for monaural singing voice separation"
    - "measuring the tendency of cnns to learn surface statistical regularities"
    - "twin networks matching the future for sequence generation"
    - "deep networks with stochastic depth"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "harmonic percussive source separation with deep neural networks and phase recovery"):
  with members:
    - "mad twinnet masker denoiser architecture with twin networks for monaural sound 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"
    - "memory augmented neural networks with wormhole connections"
    - "twin networks matching the future for sequence generation"
    - "model based stft phase recovery for audio source separation"
    - "improving neural language models with a continuous cache"
    - "topicrnn a recurrent neural network with long range semantic dependency"

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)