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 "approximate message passing algorithm with universal denoising and gaussian mixture learning"):
  Abstract: We study compressed sensing (CS) signal reconstruction problems where an input signal is measured via matrix multiplication under additive white Gaussian noise. Our signals are assumed to be stationary and ergodic, but the input statistics are unknown; the goal is to provide reconstruction algorithms that are universal to the input statistics. We present a novel algorithmic framework that combines: 1) the approximate message passing CS reconstruction framework, which solves the matrix channel recovery problem by iterative scalar channel denoising; 2) a universal denoising scheme based on context quantization, which partitions the stationary ergodic signal denoising into independent and identically distributed (i.i.d.) subsequence denoising; and 3) a density estimation approach that approximates the probability distribution of an i.i.d. sequence by fitting a Gaussian mixture (GM) model. In addition to the algorithmic framework, we provide three contributions: 1) numerical results showing that state evolution holds for nonseparable Bayesian sliding-window denoisers; 2) an i.i.d. denoiser based on a modified GM learning algorithm; and 3) a universal denoiser that does not need information about the range where the input takes values from or require the input signal to be bounded. We provide two implementations of our universal CS recovery algorithm with one being faster and the other being more accurate. The two implementations compare favorably with existing universal reconstruction algorithms in terms of both reconstruction quality and runtime.

Includes members:
  Member "capacity achieving sparse superposition codes via approximate message passing decoding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "compressive imaging via approximate message passing with image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "block coordinate regularization by denoising")
  Member "adaptive damping and mean removal for the generalized approximate message passing algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "mismatched estimation in large linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems")
  Member "inference for generalized linear models via alternating directions and bethe free energy minimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an approximate message passing framework for side information")
  Member "signal estimation with additive error metrics in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "performance limits with additive error metrics in noisy multimeasurement vector problems")
  Member "from denoising to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")
  Member "sparse estimation with the swept approximated message passing algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse bayesian learning using approximate message passing with unitary transformation")

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)