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 "inference for generalized linear models via alternating directions and bethe free energy minimization"):
  Abstract: Generalized Linear Models (GLMs), where a random vector $\mathbf{x}$ is observed through a noisy, possibly nonlinear, function of a linear transform $\mathbf{z}=\mathbf{Ax}$ arise in a range of applications in nonlinear filtering and regression. Approximate Message Passing (AMP) methods, based on loopy belief propagation, are a promising class of approaches for approximate inference in these models. AMP methods are computationally simple, general, and admit precise analyses with testable conditions for optimality for large i.i.d. transforms $\mathbf{A}$. However, the algorithms can easily diverge for general $\mathbf{A}$. This paper presents a convergent approach to the generalized AMP (GAMP) algorithm based on direct minimization of a large-system limit approximation of the Bethe Free Energy (LSL-BFE). The proposed method uses a double-loop procedure, where the outer loop successively linearizes the LSL-BFE and the inner loop minimizes the linearized LSL-BFE using the Alternating Direction Method of Multipliers (ADMM). The proposed method, called ADMM-GAMP, is similar in structure to the original GAMP method, but with an additional least-squares minimization. It is shown that for strictly convex, smooth penalties, ADMM-GAMP is guaranteed to converge to a local minima of the LSL-BFE, thus providing a convergent alternative to GAMP that is stable under arbitrary transforms. Simulations are also presented that demonstrate the robustness of the method for non-convex penalties as well.

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 "bilinear generalized approximate message passing part i derivation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "convolutional approximate message passing")
  Member "variational free energies for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inference with deep generative priors in high dimensions")
  Member "sparse estimation with the swept approximated message passing algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse bayesian learning using approximate message passing with unitary transformation")
  Member "on convergence of approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "blind sensor calibration using approximate message passing")
  Member "fixed points of generalized approximate message passing with arbitrary matrices":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bilinear recovery using adaptive vector amp")

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)