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: "a recurrent encoder decoder approach with skip filtering connections for monaural singing voice separation"
  Abstract: The objective of deep learning methods based on encoder-decoder architectures for music source separation is to approximate either ideal time-frequency masks or spectral representations of the target music source(s). The spectral representations are then used to derive time-frequency masks. In this work we introduce a method to directly learn time-frequency masks from an observed mixture magnitude spectrum. We employ recurrent neural networks and train them using prior knowledge only for the magnitude spectrum of the target source. To assess the performance of the proposed method, we focus on the task of singing voice separation. The results from an objective evaluation show that our proposed method provides comparable results to deep learning based methods which operate over complicated signal representations. Compared to previous methods that approximate time-frequency masks, our method has increased performance of signal to distortion ratio by an average of 3.8 dB.

Connected through hyperedge HE-1 (induced by source paper "investigating kernel shapes and skip connections for deep learning based harmonic percussive separation"):
  with members:
    - "harmonic percussive source separation with deep neural networks and phase recovery"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "multi scale multi band densenets for audio source separation"
    - "timbre analysis of music audio signals with convolutional neural networks"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (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"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep karaoke extracting vocals from musical mixtures using a convolutional deep neural network"

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"
    - "monaural singing voice separation with skip filtering connections and recurrent inference of time frequency mask"
    - "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 "mad twinnet masker denoiser architecture with twin networks for monaural sound source separation"):
  with members:
    - "multi scale multi band densenets for audio source separation"
    - "monaural singing voice separation with skip filtering connections and recurrent inference of time frequency mask"
    - "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-5 (induced by source paper "music source separation using stacked hourglass networks"):
  with members:
    - "multi scale multi band densenets for audio source separation"
    - "multi resolution fully convolutional neural networks for monaural audio source separation"
    - "stacked hourglass networks for human pose estimation"
    - "associative embedding end to end learning for joint detection and grouping"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "deep karaoke extracting vocals from musical mixtures using a convolutional deep neural network"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (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"
    - "monaural singing voice separation with skip filtering connections and recurrent inference of time frequency mask"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "deep neural network based speech separation optimizing an objective estimator of intelligibility for low latency applications"):
  with members:
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "theano a python framework for fast computation of mathematical expressions"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "multi resolution fully convolutional neural networks for monaural audio source separation"):
  with members:
    - "end to end source separation with adaptive front ends"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "a fully convolutional neural network for speech enhancement"
    - "discriminative enhancement for single channel audio source separation using deep neural networks"
    - "time frequency trade offs for audio source separation with binary masks"
    - "adam a method for stochastic optimization"
    - "source separation using regularized nmf with mmse estimates under gmm priors with online learning for the uncertainties"

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)