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 "restoration by compression"):
  Abstract: In this paper we study the topic of signal restoration using complexity regularization, quantifying the compression bit-cost of the signal estimate. While complexity-regularized restoration is an established concept, solid practical methods were suggested only for the Gaussian denoising task, leaving more complicated restoration problems without a generally constructive approach. Here we present practical methods for complexity-regularized restoration of signals, accommodating deteriorations caused by a known linear degradation operator of an arbitrary form. Our iterative procedure, obtained using the Half Quadratic Splitting approach, addresses the restoration task as a sequence of simpler problems involving $ \ell_2$-regularized estimations and rate-distortion optimizations (considering the squared-error criterion). Further, we replace the rate-distortion optimizations with an arbitrary standardized compression technique and thereby restore the signal by leveraging underlying models designed for compression. Additionally, we propose a shift-invariant complexity regularizer, measuring the bit-cost of all the shifted forms of the estimate, extending our method to use averaging of decompressed outputs gathered from compression of shifted signals. On the theoretical side, we present an analysis of complexity-regularized restoration of a cyclo-stationary Gaussian signal from deterioration by a linear shift-invariant operator and an additive white Gaussian noise. The theory shows that optimal complexity-regularized restoration relies on an elementary restoration filter and compression spreading reconstruction quality unevenly based on the energy distribution of the degradation filter. Nicely, these ideas are realized also in the proposed practical methods. Finally, we present experiments showing good results for image deblurring and inpainting using the HEVC compression standard.

Includes members:
  Member "an efficient algorithm for compression based compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "snapshot compressed sensing performance bounds and algorithms")
  Member "plug and play priors for bright field electron tomography and sparse interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "the little engine that could regularization by denoising red":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ada lista learned solvers adaptive to varying models")
  Member "postprocessing of compressed images via sequential denoising":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "near lossless ell_ infty constrained image decompression via deep neural network")
  Member "poisson inverse problems by the plug and play scheme":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "image processing using smooth ordering of its patches":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neumann networks for inverse problems in imaging")

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)