human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "improved multipath time delay estimation using cepstrum subtraction"):
  Abstract: When a motor-powered vessel travels past a fixed hydrophone in a multipath environment, a Lloyd's mirror constructive/destructive interference pattern is observed in the output spectrogram. The power cepstrum detects the periodic structure of the Lloyd's mirror pattern by generating a sequence of pulses (rahmonics) located at the fundamental quefrency (periodic time) and its multiples. This sequence is referred to here as the `rahmonic component' of the power cepstrum. The fundamental quefrency, which is the reciprocal of the frequency difference between adjacent interference fringes, equates to the multipath time delay. The other component of the power cepstrum is the non-rahmonic (extraneous) component, which combines with the rahmonic component to form the (total) power cepstrum. A data processing technique, termed `cepstrum subtraction', is described. This technique suppresses the extraneous component of the power cepstrum, leaving the rahmonic component that contains the desired multipath time delay information. This technique is applied to real acoustic recordings of motor-vessel transits in a shallow water environment, where the broadband noise radiated by the vessel arrives at the hydrophone via a direct ray path and a time-delayed multipath. The results show that cepstrum subtraction improves multipath time delay estimation by a factor of two for the at-sea experiment. #R##N#keywords - time delay estimation, underwater acoustics, cepstrum, source localization, autocorrelation

Includes members:
  Member "sound source localization in a multipath environment using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi source doa estimation through pattern recognition of the modal coherence of a reverberant soundfield")
  Member "convolutional neural networks for passive monitoring of a shallow water environment using a single sensor":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sound source localization in a multipath environment using convolutional neural networks")

Question: Please tell me which class the source paper 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)