human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on the convergence of approximate message passing with arbitrary matrices"
  Abstract: Approximate message passing (AMP) methods and their variants have attracted considerable recent attention for the problem of estimating a random vector $\mathbf{x}$ observed through a linear transform $\mathbf{A}$. In the case of large i.i.d. zero-mean Gaussian $\mathbf{A}$, the methods exhibit fast convergence with precise analytic characterizations on the algorithm behavior. However, the convergence of AMP under general transforms $\mathbf{A}$ is not fully understood. In this paper, we provide sufficient conditions for the convergence of a damped version of the generalized AMP (GAMP) algorithm in the case of quadratic cost functions (i.e., Gaussian likelihood and prior). It is shown that, with sufficient damping, the algorithm is guaranteed to converge, although the amount of damping grows with peak-to-average ratio of the squared singular values of the transforms $\mathbf{A}$. This result explains the good performance of AMP on i.i.d. Gaussian transforms $\mathbf{A}$, but also their difficulties with ill-conditioned or non-zero-mean transforms $\mathbf{A}$. A related sufficient condition is then derived for the local stability of the damped GAMP method under general cost functions, assuming certain strict convexity conditions.

Connected through hyperedge HE-1 (induced by source paper "convolutional approximate message passing"):
  with members:
    - "a unified framework of state evolution for message passing algorithms"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "sparse bayesian learning using approximate message passing with unitary transformation"):
  with members:
    - "a gamp based low complexity sparse bayesian learning algorithm"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "approximate message passing with unitary transformation"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "orthogonal transform multiplexing with memoryless nonlinearity a possible alternative to traditional coded modulation schemes"):
  with members:
    - "detection of nonlinearly distorted ofdm signals via generalized approximate message passing"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-4 (induced by source paper "parametric bilinear generalized approximate message passing"):
  with members:
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "self calibration and biconvex compressive sensing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "identifiability in blind deconvolution under minimal assumptions"
    - "identifiability and stability in blind deconvolution under minimal assumptions"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "fundamental limits of blind deconvolution part ii sparsity ambiguity trade offs"

Connected through hyperedge HE-5 (induced by source paper "a survey of stochastic simulation and optimization methods in signal processing"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "communication efficient distributed dual coordinate ascent"
    - "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"

Connected through hyperedge HE-6 (induced by source paper "vector approximate message passing for the generalized linear model"):
  with members:
    - "hybrid approximate message passing"
    - "expectation consistent approximate inference generalizations and convergence"
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "learning and free energies for vector approximate message passing"
    - "denoising based vector approximate message passing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "s amp approximate message passing for general matrix ensembles"

Connected through hyperedge HE-7 (induced by source paper "sparse multinomial logistic regression via approximate message passing"):
  with members:
    - "hybrid approximate message passing"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "parameterless optimal approximate message passing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "hybrid approximate message passing"
    - "hybrid approximate message passing with applications to structured sparsity"

Connected through hyperedge HE-8 (induced by source paper "inference for generalized linear models via alternating directions and bethe free energy minimization"):
  with members:
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "bilinear generalized approximate message passing part i derivation"
    - "variational free energies for compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "on convergence of approximate message passing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"

Question: Please tell me which class the center node 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)