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 "isar image analysis and recovery with unavailable or heavily corrupted data"):
  Abstract: Common inverse synthetic aperture radar (ISAR) images and signals can be reconstructed from fewer samples than the sampling theorem requires because they are usually sparse. Unavailable randomly positioned samples can result from heavily corrupted parts of the signal. Because these samples can be omitted and declared as unavailable, the application of the compressive sensing methods in the recovery of heavily corrupted signal and radar images is possible. A simple direct method for the recovery of unavailable signal samples and the calculation of the restored ISAR image is reviewed. An analysis of the noise influence is performed. For fast-maneuvering ISAR targets, the sparsity property is lost because the ISAR image is blurred. A nonparametric quadratic time-frequency representation-based method is used to restore the ISAR image sparsity. However, the linear relation between the signal and the sparsity domain transformation is lost. A recently proposed gradient recovery algorithm is adapted for this kind of analysis. It does not require a linear relation of the signal and its sparsity domain transformation in the process of unavailable data recovery. The presented methods and results are tested on several examples proving the expected accuracy and improvements.

Includes members:
  Member "reconstruction of randomly sampled sparse signals using an adaptive gradient algorithm":
    also connected through other hyperedges:
      (none)
  Member "adaptive variable step algorithm for missing samples recovery in sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a convex similarity index for sparse recovery of missing image samples")
  Member "beyond nyquist efficient sampling of sparse bandlimited signals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")
  Member "from theory to practice sub nyquist sampling of sparse wideband analog signals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "image guided depth sampling and reconstruction")

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.IT(Information Theory)