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: "music source separation using stacked hourglass networks"
  Abstract: In this paper, we propose a simple yet effective method for multiple music source separation using convolutional neural networks. Stacked hourglass network, which was originally designed for human pose estimation in natural images, is applied to a music source separation task. The network learns features from a spectrogram image across multiple scales and generates masks for each music source. The estimated mask is refined as it passes over stacked hourglass modules. The proposed framework is able to separate multiple music sources using a single network. Experimental results on MIR-1K and DSD100 datasets validate that the proposed method achieves competitive results comparable to the state-of-the-art methods in multiple music source separation and singing voice separation tasks.

Connected through hyperedge HE-1 (induced by source paper "audio query based music source separation"):
  with members:
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "end to end sound source separation conditioned on instrument labels"
    - "a style based generator architecture for generative adversarial networks"
    - "class conditional embeddings for music source separation"
    - "deep extractor network for target speaker recovery from single channel speech mixtures"
    - "sampling generative networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "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"
    - "wave u net a multi scale neural network for end to end audio 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"
    - "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 "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"
    - "mad twinnet masker denoiser architecture with twin networks for monaural sound source separation"
    - "vocal melody extraction using patch based cnn"
    - "transcribing lyrics from commercial song audio the first step towards singing content processing"
    - "multi scale multi band densenets for audio source separation"

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)