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: "an overview of lead and accompaniment separation in music"
  Abstract: Popular music is often composed of an accompaniment and a lead component, the latter typically consisting of vocals. Filtering such mixtures to extract one or both components has many applications, such as automatic karaoke and remixing. This particular case of source separation yields very specific challenges and opportunities, including the particular complexity of musical structures, but also relevant prior knowledge coming from acoustics, musicology or sound engineering. Due to both its importance in applications and its challenging difficulty, lead and accompaniment separation has been a popular topic in signal processing for decades. In this article, we provide a comprehensive review of this research topic, organizing the different approaches according to whether they are model-based or data-centered. For model-based methods, we organize them according to whether they concentrate on the lead signal, the accompaniment, or both. For data-centered approaches, we discuss the particular difficulty of obtaining data for learning lead separation systems, and then review recent approaches, notably those based on deep learning. Finally, we discuss the delicate problem of evaluating the quality of music separation through adequate metrics and present the results of the largest evaluation, to-date, of lead and accompaniment separation systems. In conjunction with the above, a comprehensive list of references is provided, along with relevant pointers to available implementations and repositories.

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"
    - "denoising auto encoder with recurrent skip connections and residual regression for music source separation"
    - "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 "score and lyrics free singing voice generation"):
  with members:
    - "dilated convolution with dilated gru for music source separation"
    - "data efficient voice cloning for neural singing synthesis"
    - "high fidelity speech synthesis with adversarial networks"
    - "adversarially trained end to end korean singing voice synthesis system"
    - "gansynth adversarial neural audio synthesis"
    - "group normalization"
    - "revisiting singing voice detection a quantitative review and the future outlook"
    - "sing symbol to instrument neural generator"

Connected through hyperedge HE-3 (induced by source paper "dilated convolution with dilated gru for music source separation"):
  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"
    - "denoising auto encoder with recurrent skip connections and residual regression for music 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-4 (induced by source paper "improving singing voice separation with the wave u net using minimum hyperspherical energy"):
  with members:
    - "learning towards minimum hyperspherical energy"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "diversity promoting bayesian learning of latent variable models"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (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"
    - "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"
    - "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-6 (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"
    - "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"
    - "singing voice separation and vocal f0 estimation based on mutual combination of robust principal component analysis and subharmonic summation"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"

Connected through hyperedge HE-7 (induced by source paper "end to end sound source separation conditioned on instrument labels"):
  with members:
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "class conditional embeddings for music source separation"
    - "creating a musical performance dataset for multimodal music analysis challenges insights and applications"
    - "u net convolutional networks for biomedical image 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)