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: "mad twinnet masker denoiser architecture with twin networks for monaural sound source separation"
  Abstract: Monaural singing voice separation task focuses on the prediction of the singing voice from a single channel music mixture signal. Current state of the art (SOTA) results in monaural singing voice separation are obtained with deep learning based methods. In this work we present a novel deep learning based method that learns long-term temporal patterns and structures of a musical piece. We build upon the recently proposed Masker-Denoiser (MaD) architecture and we enhance it with the Twin Networks, a technique to regularize a recurrent generative network using a backward running copy of the network. We evaluate our method using the Demixing Secret Dataset and we obtain an increment to signal-to-distortion ratio (SDR) of 0.37 dB and to signal-to-interference ratio (SIR) of 0.23 dB, compared to previous SOTA results.

Connected through hyperedge HE-1 (induced by source paper "denoising auto encoder with recurrent skip connections and residual regression for music source separation"):
  with members:
    - "frame level instrument recognition by timbre and pitch"
    - "an overview of lead and accompaniment separation in music"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "singing style transfer using cycle consistent boundary equilibrium generative adversarial networks"
    - "vocal melody extraction using patch based cnn"
    - "music source separation using stacked hourglass networks"
    - "transcribing lyrics from commercial song audio the first step towards singing content processing"
    - "multi scale multi band densenets for audio source separation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "monaural singing voice separation with skip filtering connections and recurrent inference of time frequency mask"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

Connected through hyperedge HE-3 (induced by source paper "harmonic percussive source separation with deep neural networks and phase recovery"):
  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"
    - "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"

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)