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: "complex cepstrum based decomposition of speech for glottal source estimation"
  Abstract: Homomorphic analysis is a well-known method for the separation of non-linearly combined signals. More particularly, the use of complex cepstrum for source-tract deconvolution has been discussed in various articles. However there exists no study which proposes a glottal flow estimation methodology based on cepstrum and reports effective results. In this paper, we show that complex cepstrum can be effectively used for glottal flow estimation by separating the causal and anticausal components of a windowed speech signal as done by the Zeros of the Z-Transform (ZZT) decomposition. Based on exactly the same principles presented for ZZT decomposition, windowing should be applied such that the windowed speech signals exhibit mixed-phase characteristics which conform the speech production model that the anticausal component is mainly due to the glottal flow open phase. The advantage of the complex cepstrum-based approach compared to the ZZT decomposition is its much higher speed.

Connected through hyperedge HE-1 (induced by source paper "excitation based voice quality analysis and modification"):
  with members:
    - "glottal closure and opening instant detection from speech signals"
    - "a deterministic plus stochastic model of the residual signal for improved parametric speech synthesis"

Connected through hyperedge HE-2 (induced by source paper "phase based information for voice pathology detection"):
  with members:
    - "on the mutual information between source and filter contributions for voice pathology detection"

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"
    - "glottal closure and opening instant detection from speech signals"
    - "causal anticausal decomposition of speech using complex cepstrum for glottal source estimation"
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "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 speech signals a quantitative review"):
  with members:
    - "glottal closure and opening instant detection from speech signals"
    - "a deterministic plus stochastic model of the residual signal for improved parametric speech synthesis"

Connected through hyperedge HE-5 (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"
    - "glottal closure and opening instant detection from speech signals"

Connected through hyperedge HE-6 (induced by source paper "causal anticausal decomposition of speech using complex cepstrum for glottal source estimation"):
  with members:
    - "glottal closure and opening instant detection from speech signals"

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)