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: "glottal source processing from analysis to applications"
  Abstract: The great majority of current voice technology applications relies on acoustic features characterizing the vocal tract response, such as the widely used MFCC of LPC parameters. Nonetheless, the airflow passing through the vocal folds, and called glottal flow, is expected to exhibit a relevant complementarity. Unfortunately, glottal analysis from speech recordings requires specific and more complex processing operations, which explains why it has been generally avoided. This review gives a general overview of techniques which have been designed for glottal source processing. Starting from fundamental analysis tools of pitch tracking, glottal closure instant detection, glottal flow estimation and modelling, this paper then highlights how these solutions can be properly integrated within various voice technology applications.

Connected through hyperedge HE-1 (induced by source paper "voice conversion for whispered speech synthesis"):
  with members:
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "lstm based whisper detection"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "learning both weights and connections for efficient neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "traditional machine learning for pitch detection"):
  with members:
    - "glottal closure and opening instant detection from speech signals"
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "a comparative study of pitch extraction algorithms on a large variety of singing sounds"
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "voice activity detection merging source and filter based information"

Connected through hyperedge HE-3 (induced by source paper "voice activity detection merging source and filter based information"):
  with members:
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "a comparative study of glottal source estimation techniques"

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)