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: "sparsity based algorithm for detecting faults in rotating machines"
  Abstract: Abstract   This paper addresses the detection of periodic transients in vibration signals so as to detect faults in rotating machines. For this purpose, we present a method to estimate periodic-group-sparse signals in noise. The method is based on the formulation of a convex optimization problem. A fast iterative algorithm is given for its solution. A simulated signal is formulated to verify the performance of the proposed approach for periodic feature extraction. The detection performance of comparative methods is compared with that of the proposed approach via RMSE values and receiver operating characteristic (ROC) curves. Finally, the proposed approach is applied to single fault diagnosis of a locomotive bearing and compound faults diagnosis of motor bearings. The processed results show that the proposed approach can effectively detect and extract the useful features of bearing outer race and inner race defect.

Connected through hyperedge HE-1 (induced by source paper "repetitive transients extraction algorithm for detecting bearing faults"):
  with members:
    - "sparsity based correction of exponential artifacts"
    - "group sparse signal denoising non convex regularization convex optimization"
    - "sparse signal estimation by maximally sparse convex optimization"

Connected through hyperedge HE-2 (induced by source paper "enhanced low rank matrix approximation"):
  with members:
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "convex denoising using non convex tight frame regularization"
    - "a note on patch based low rank minimization for fast image denoising"
    - "transformed schatten 1 iterative thresholding algorithms for low rank matrix completion"
    - "transformed schatten 1 iterative thresholding algorithms for matrix rank minimization and applications"
    - "patch based low rank minimization for image denoising"
    - "group sparse signal denoising non convex regularization convex optimization"

Connected through hyperedge HE-3 (induced by source paper "periodic transients separation algorithm for detecting bearing faults"):
  with members:
    - "sparsity based correction of exponential artifacts"
    - "group sparse signal denoising non convex regularization convex optimization"
    - "sparse signal estimation by maximally sparse convex optimization"

Connected through hyperedge HE-4 (induced by source paper "detection of faults in rotating machinery using periodic time frequency sparsity"):
  with members:
    - "sparsity based correction of exponential artifacts"
    - "translation invariant shrinkage thresholding of group sparse signals"
    - "group sparse signal denoising non convex regularization convex optimization"

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)