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 closure and opening instant detection from speech signals"
  Abstract: This paper proposes a new procedure to detect Glottal Closure and Opening Instants (GCIs and GOIs) directly from speech waveforms. The procedure is divided into two successive steps. First a mean-based signal is computed, and intervals where speech events are expected to occur are extracted from it. Secondly, at each interval a precise position of the speech event is assigned by locating a discontinuity in the Linear Prediction residual. The proposed method is compared to the DYPSA algorithm on the CMU ARCTIC database. A significant improvement as well as a better noise robustness are reported. Besides, results of GOI identification accuracy are promising for the glottal source characterization.

Connected through hyperedge HE-1 (induced by source paper "excitation based voice quality analysis and modification"):
  with members:
    - "complex cepstrum based decomposition of speech for glottal source estimation"
    - "a deterministic plus stochastic model of the residual signal for improved parametric speech synthesis"

Connected through hyperedge HE-2 (induced by source paper "a comparative evaluation of pitch modification techniques"):
  with members:
    - "a deterministic plus stochastic model of the residual signal for improved parametric speech synthesis"

Connected through hyperedge HE-3 (induced by source paper "glottal source processing from analysis to applications"):
  with members:
    - "on the mutual information between source and filter contributions for voice pathology detection"
    - "a comparative evaluation of pitch modification techniques"
    - "phase based information for voice pathology detection"
    - "the deterministic plus stochastic model of the residual signal and its applications"
    - "causal anticausal decomposition of speech using complex cepstrum for glottal source estimation"
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "complex cepstrum based decomposition of speech for glottal source estimation"
    - "detection of glottal closure instants from speech signals a quantitative review"

Connected through hyperedge HE-4 (induced by source paper "detection of glottal closure instants from raw speech using convolutional neural networks"):
  with members:
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "glottal closure instants detection from pathological acoustic speech signal using deep learning"
    - "wavenet a generative model for raw audio"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "estimating phoneme class conditional probabilities from raw speech signal using convolutional neural networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-5 (induced by source paper "the deterministic plus stochastic model of the residual signal and its applications"):
  with members:
    - "a comparative evaluation of pitch modification techniques"
    - "a comparative study of glottal source estimation techniques"
    - "a deterministic plus stochastic model of the residual signal for improved parametric speech synthesis"

Connected through hyperedge HE-6 (induced by source paper "detection of glottal closure instants from speech signals a quantitative review"):
  with members:
    - "complex cepstrum based decomposition of speech for glottal source estimation"
    - "a deterministic plus stochastic model of the residual signal for improved parametric speech synthesis"

Connected through hyperedge HE-7 (induced by source paper "a comparative study of glottal source estimation techniques"):
  with members:
    - "on the mutual information between source and filter contributions for voice pathology detection"
    - "complex cepstrum based decomposition of speech for glottal source estimation"

Connected through hyperedge HE-8 (induced by source paper "causal anticausal decomposition of speech using complex cepstrum for glottal source estimation"):
  with members:
    - "complex cepstrum based decomposition of speech for glottal source estimation"

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)