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 "compressive phase retrieval via generalized approximate message passing"):
  Abstract: In phase retrieval, the goal is to recover a signal x ∈ C#N# N#N# from the magnitudes of linear measurements Ax ∈ C#N# M#N#. While recent theory has established that M ≈ 4N intensity measurements are necessary and sufficient to recover generic x, there is great interest in reducing the number of measurements through the exploitation of sparse \mbi x, which is known as compressive phase retrieval. In this work, we detail a novel, probabilistic approach to compressive phase retrieval based on the generalized approximate message passing (GAMP) algorithm. We then present a numerical study of the proposed PR-GAMP algorithm, demonstrating its excellent phase-transition behavior, robustness to noise, and runtime. Our experiments suggest that approximately M ≥ 2 Klog#N# 2#N#(N/K) intensity measurements suffice to recover K-sparse Bernoulli-Gaussian signals for \mbi A with i.i.d Gaussian entries and K ≪ N. Meanwhile, when recovering a 6678-sparse 65536-pixel grayscale image from 32768 randomly masked and blurred Fourier intensity measurements at 30 dB measurement SNR, PR-GAMP achieved an output SNR of no less than 28 dB in all of 100 random trials, with a median runtime of only 7.3 seconds. Compared to the recently proposed CPRL, sparse-Fienup, and GESPAR algorithms, our experiments suggest that PR-GAMP has a superior phase transition and orders-of-magnitude faster runtimes as the sparsity and problem dimensions increase.

Includes members:
  Member "adaptive damping and mean removal for the generalized approximate message passing algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "an empirical bayes approach to recovering linearly constrained non negative sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning and free energies for vector approximate message passing")
  Member "on the convergence of approximate message passing with arbitrary matrices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convolutional approximate message passing")
  Member "variational free energies for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inference with deep generative priors in high dimensions")
  Member "exact and stable covariance estimation from quadratic sampling via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "fixed points of generalized approximate message passing with arbitrary matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bilinear recovery using adaptive vector amp")
  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 "gespar efficient phase retrieval of sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "serial quantization for sparse time sequences")

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)