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: "singing voice separation and vocal f0 estimation based on mutual combination of robust principal component analysis and subharmonic summation"
  Abstract: This paper presents a new method of singing voice analysis that performs mutually-dependent singing voice separation and vocal fundamental frequency F0 estimation. Vocal F0 estimation is considered to become easier if singing voices can be separated from a music audio signal, and vocal F0 contours are useful for singing voice separation. This calls for an approach that improves the performance of each of these tasks by using the results of the other. The proposed method first performs robust principal component analysis RPCA for roughly extracting singing voices from a target music audio signal. The F0 contour of the main melody is then estimated from the separated singing voices by finding the optimal temporal path over an F0 saliency spectrogram. Finally, the singing voices are separated again more accurately by combining a conventional time-frequency mask given by RPCA with another mask that passes only the harmonic structures of the estimated F0s. Experimental results showed that the proposed method significantly improved the performances of both singing voice separation and vocal F0 estimation. The proposed method also outperformed all the other methods of singing voice separation submitted to an international music analysis competition called MIREX 2014.

Connected through hyperedge HE-1 (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"
    - "monaural singing voice separation with skip filtering connections and recurrent inference of time frequency mask"
    - "single channel multi speaker separation using deep clustering"
    - "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-2 (induced by source paper "singing voice separation using a deep convolutional neural network trained by ideal binary mask and cross entropy"):
  with members:
    - "jointly detecting and separating singing voice a multi task approach"
    - "an overview of lead and accompaniment separation in music"
    - "a functional taxonomy of music generation systems"
    - "svsgan singing voice separation via generative adversarial network"
    - "exploiting saliency for object segmentation from image level labels"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "densely connected convolutional networks"
    - "joint optimization of masks and deep recurrent neural networks for monaural 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)