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 "bernoulli gaussian approximate message passing algorithm for compressed sensing with 1d finite difference sparsity"):
  Abstract: This paper proposes a fast approximate message-passing (AMP) algorithm for solving compressed sensing (CS) recovery problems with 1D-finite-difference sparsity in term of MMSE estimation. The proposed algorithm, named ssAMP-BGFD, is low-computational with its fast convergence and cheap per-iteration cost, providing phase transition nearly approaching to the state-of-the-art. The proposed algorithm is originated from a sum-product message-passing rule, applying a Bernoulli-Gaussian (BG) prior, seeking an MMSE solution. The algorithm construction includes not only the conventional AMP technique for the measurement fidelity, but also suggests a simplified message-passing method to promote the signal sparsity in finite-difference. Furthermore, we provide an EM-tuning methodology to learn the BG prior parameters, suggesting how to use some practical measurement matrices satisfying the RIP requirement under the ssAMP-BGFD recovery. Extensive empirical results confirms performance of the proposed algorithm, in phase transition, convergence speed, and CPU runtime, compared to the recent algorithms.

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 "on the convergence of approximate message passing with arbitrary matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional approximate message passing")
  Member "from denoising to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")
  Member "generalized approximate message passing for cosparse analysis compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "beamspace channel estimation for massive mimo mmwave systems algorithm and vlsi design")
  Member "expectation maximization gaussian mixture approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo")
  Member "approximate message passing with consistent parameter estimation and applications to sparse learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "living near the edge a lower bound on the phase transition of total variation minimization")

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)