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: "denoising auto encoder with recurrent skip connections and residual regression for music source separation"
  Abstract: Convolutional neural networks with skip connections have shown good performance in music source separation. In this work, we propose a denoising Auto-encoder with Recurrent skip Connections (ARC). We use 1D convolution along the temporal axis of the time-frequency feature map in all layers of the fully-convolutional network. The use of 1D convolution makes it possible to apply recurrent layers to the intermediate outputs of the convolution layers. In addition, we also propose an enhancement network and a residual regression method to further improve the separation result. The recurrent skip connections, the enhancement module, and the residual regression all improve the separation quality. The ARC model with residual regression achieves 5.74 siganl-to-distoration ratio (SDR) in vocals with MUSDB in SiSEC 2018. We also evaluate the ARC model alone on the older dataset DSD100 (used in SiSEC 2016) and it achieves 5.91 SDR in vocals.

Connected through hyperedge HE-1 (induced by source paper "addressing the confounds of accompaniments in singer identification"):
  with members:
    - "score and lyrics free singing voice generation"
    - "dilated convolution with dilated gru for music source separation"
    - "learning a joint embedding space of monophonic and mixed music signals for singing voice"
    - "an overview of lead and accompaniment separation in music"
    - "understanding data augmentation for classification when to warp"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (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"
    - "progressive growing of gans for improved quality stability and variation"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-3 (induced by source paper "music source separation in the waveform domain"):
  with members:
    - "unsupervised singing voice conversion"
    - "fixup initialization residual learning without normalization"
    - "sing symbol to instrument neural generator"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "end to end music source separation is it possible in the waveform domain"
    - "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"

Connected through hyperedge HE-4 (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"
    - "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"
    - "dilated recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "spectrogram channels u net a source separation model viewing each channel as the spectrogram of each source"):
  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"
    - "segan speech enhancement generative adversarial network"
    - "svsgan singing voice separation via generative adversarial network"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "singing style transfer using cycle consistent boundary equilibrium generative adversarial networks"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "fully convolutional networks for semantic segmentation"

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)