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: "revisiting singing voice detection a quantitative review and the future outlook"
  Abstract: Since the vocal component plays a crucial role in popular music, singing voice detection has been an active research topic in music information retrieval. Although several proposed algorithms have shown high performances, we argue that there still is a room to improve to build a more robust singing voice detection system. In order to identify the area of improvement, we first perform an error analysis on three recent singing voice detection systems. Based on the analysis, we design novel methods to test the systems on multiple sets of internally curated and generated data to further examine the pitfalls, which are not clearly revealed with the current datasets. From the experiment results, we also propose several directions towards building a more robust singing voice detector.

Connected through hyperedge HE-1 (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"
    - "an overview of lead and accompaniment separation in music"
    - "group normalization"
    - "sing symbol to instrument neural generator"

Connected through hyperedge HE-2 (induced by source paper "gan based generation and automatic selection of explanations for neural networks"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "improved training of wasserstein gans"
    - "fma a dataset for music analysis"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "learning a joint embedding space of monophonic and mixed music signals for singing voice"):
  with members:
    - "disambiguating music artists at scale with audio metric learning"
    - "a hybrid of deep audio feature and i vector for artist recognition"
    - "learning from between class examples for deep sound recognition"
    - "deep metric learning with angular loss"
    - "representation learning of music using artist labels"
    - "embedding label structures for fine grained feature representation"

Connected through hyperedge HE-4 (induced by source paper "multitask learning for frame level instrument recognition"):
  with members:
    - "frame level instrument recognition by timbre and pitch"
    - "weakly supervised visual instrument playing action detection in videos"
    - "u net convolutional networks for biomedical image segmentation"
    - "automatic instrument recognition in polyphonic music using convolutional neural networks"

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)