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 "from denoising to compressed sensing"):
  Abstract: A denoising algorithm seeks to remove noise, errors, or perturbations from a signal. Extensive research has been devoted to this arena over the last several decades, and as a result, todays denoisers can effectively remove large amounts of additive white Gaussian noise. A compressed sensing (CS) reconstruction algorithm seeks to recover a structured signal acquired using a small number of randomized measurements. Typical CS reconstruction algorithms can be cast as iteratively estimating a signal from a perturbed observation. This paper answers a natural question: How can one effectively employ a generic denoiser in a CS reconstruction algorithm? In response, we develop an extension of the approximate message passing (AMP) framework, called Denoising-based AMP (D-AMP), that can integrate a wide class of denoisers within its iterations. We demonstrate that, when used with a high performance denoiser for natural images, D-AMP offers state-of-the-art CS recovery performance while operating tens of times faster than competing methods. We explain the exceptional performance of D-AMP by analyzing some of its theoretical features. A key element in DAMP is the use of an appropriate Onsager correction term in its iterations, which coerces the signal perturbation at each iteration to be very close to the white Gaussian noise that denoisers are typically designed to remove.

Includes members:
  Member "compressive imaging via approximate message passing with image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "block coordinate regularization by denoising")
  Member "does ell_p minimization outperform ell_1 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal data detection in large mimo")
  Member "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "bilinear generalized approximate message passing part i derivation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "s amp approximate message passing for general matrix ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "signal recovery using expectation consistent approximation for linear observations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "macroscopic analysis of vector approximate message passing in a model mismatch setting")
  Member "generalized approximate message passing for cosparse analysis compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "beamspace channel estimation in mmwave systems via cosparse image reconstruction technique")
  Member "parameterless optimal approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "beamspace channel estimation for massive mimo mmwave systems algorithm and vlsi design")

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)