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: "denoising based vector approximate message passing"
  Abstract: The denoising-based approximate message passing (D-AMP) methodology, recently proposed by Metzler, Maleki, and Baraniuk, allows one to plug in sophisticated denoisers like BM3D into the AMP algorithm to achieve state-of-the-art compressive image recovery. But AMP diverges with small deviations from the i.i.d.-Gaussian assumption on the measurement matrix. Recently, the vector AMP (VAMP) algorithm has been proposed to fix this problem. In this work, we show that the benefits of VAMP extend to D-VAMP.

Connected through hyperedge HE-1 (induced by source paper "plug and play methods for magnetic resonance imaging"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "image reconstruction by domain transform manifold learning"
    - "high quality automatic foreground extraction using consensus equilibrium"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "hybrid approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "plug and play methods for magnetic resonance imaging long version"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "solving inverse computational imaging problems using deep pixel level prior"
    - "image reconstruction by domain transform manifold learning"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-3 (induced by source paper "mc ista net adaptive measurement and initialization and channel attention optimization inspired neural network for compressive sensing"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "image super resolution using very deep residual channel attention networks"
    - "ensemble one dimensional convolution neural networks for skeleton based action recognition"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "learning deep cnn denoiser prior for image restoration"
    - "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
    - "deep residual learning for image recognition"
    - "new constructions of rip matrices with fast multiplication and fewer rows"

Connected through hyperedge HE-4 (induced by source paper "regularization by denoising clarifications and new interpretations"):
  with members:
    - "local kernels that approximate bayesian regularization and proximal operators"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "scene adapted plug and play algorithm with guaranteed convergence applications to data fusion in imaging"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "acceleration of red via vector extrapolation"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "hybrid approximate message passing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-5 (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"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "expectation maximization gaussian mixture approximate message passing"

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"
    - "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"
    - "on the convergence of approximate message passing with arbitrary matrices"

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)