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: "dynamic compressive sensing of time varying signals via approximate message passing"
  Abstract: In this work the dynamic compressive sensing (CS) problem of recovering sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear measurements is explored from a Bayesian perspective. While there has been a handful of previously proposed Bayesian dynamic CS algorithms in the literature, the ability to perform inference on high-dimensional problems in a computationally efficient manner remains elusive. In response, we propose a probabilistic dynamic CS signal model that captures both amplitude and support correlation structure, and describe an approximate message passing algorithm that performs soft signal estimation and support detection with a computational complexity that is linear in all problem dimensions. The algorithm, DCS-AMP, can perform either causal filtering or non-causal smoothing, and is capable of learning model parameters adaptively from the data through an expectation-maximization learning procedure. We provide numerical evidence that DCS-AMP performs within 3 dB of oracle bounds on synthetic data under a variety of operating conditions. We further describe the result of applying DCS-AMP to two real dynamic CS datasets, as well as a frequency estimation task, to bolster our claim that DCS-AMP is capable of offering state-of-the-art performance and speed on real-world high-dimensional problems.

Connected through hyperedge HE-1 (induced by source paper "an approximate message passing framework for side information"):
  with members:
    - "an analysis of state evolution for approximate message passing with side information"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "state evolution for approximate message passing with non separable functions"
    - "rapid fading due to human blockage in pedestrian crowds at 5g millimeter wave frequencies"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "optimal trade offs in multi processor approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "an analysis of state evolution for approximate message passing with side information"):
  with members:
    - "an approximate message passing framework for side information"
    - "finite sample analysis of approximate message passing algorithms"
    - "state evolution for approximate message passing with non separable functions"
    - "approximate message passing based compressed sensing reconstruction with generalized elastic net prior"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "iterated extended kalman smoother based variable splitting for l_1 regularized state estimation"):
  with members:
    - "image fusion with cosparse analysis operator"
    - "doubly robust smoothing of dynamical processes via outlier sparsity constraints"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "a unified bayesian inference framework for generalized linear models"):
  with members:
    - "hybrid approximate message passing"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "d oamp a denoising based signal recovery algorithm for compressed sensing"
    - "vector approximate message passing for the generalized linear model"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"
    - "denoising based vector approximate message passing"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "efficient high dimensional inference in the multiple measurement vector problem"

Connected through hyperedge HE-5 (induced by source paper "structured turbo compressed sensing for downlink massive mimo ofdm channel estimation"):
  with members:
    - "denoising based turbo compressed sensing"
    - "approximate message passing with nearest neighbor sparsity pattern learning"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "fundamental limits of training based multiuser mimo systems"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"

Connected through hyperedge HE-6 (induced by source paper "hybrid approximate message passing"):
  with members:
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "decoding from pooled data phase transitions of message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "sparse multinomial logistic regression via approximate message passing"
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "learning and free energies for vector approximate message passing"

Connected through hyperedge HE-7 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review"):
  with members:
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "fpa cs focal plane array based compressive imaging in short wave infrared"
    - "quadratic basis pursuit"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "lensless imaging by compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "learning and free energies for vector approximate message passing"):
  with members:
    - "hybrid approximate message passing"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "the mutual information in random linear estimation"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "binary linear classification and feature selection via generalized approximate message passing"

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)