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 "detection of faults in rotating machinery using periodic time frequency sparsity"):
  Abstract: Abstract   This paper addresses the problem of extracting periodic oscillatory features in vibration signals for detecting faults in rotating machinery. To extract the feature, we propose an approach in the short-time Fourier transform (STFT) domain where the periodic oscillatory feature manifests itself as a relatively sparse grid. To estimate the sparse grid, we formulate an optimization problem using customized binary weights in the regularizer, where the weights are formulated to promote periodicity. In order to solve the proposed optimization problem, we develop an algorithm called augmented Lagrangian majorization–minimization algorithm, which combines the split augmented Lagrangian shrinkage algorithm (SALSA) with majorization–minimization (MM), and is guaranteed to converge for both convex and non-convex formulation. As examples, the proposed approach is applied to simulated data, and used as a tool for diagnosing faults in bearings and gearboxes for real data, and compared to some state-of-the-art methods. The results show that the proposed approach can effectively detect and extract the periodical oscillatory features.

Includes members:
  Member "sparsity based algorithm for detecting faults in rotating machines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "repetitive transients extraction algorithm for detecting bearing faults")
  Member "sparsity based correction of exponential artifacts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "linnea automatic generation of efficient linear algebra programs")
  Member "translation invariant shrinkage thresholding of group sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "total variation with overlapping group sparsity and lp quasinorm for infrared image deblurring under salt and pepper noise")
  Member "group sparse signal denoising non convex regularization convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "total variation with overlapping group sparsity and lp quasinorm for infrared image deblurring under salt and pepper noise")

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)