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: "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
  Abstract: Estimating a vector $\mathbf{x}$ from noisy linear measurements $\mathbf{Ax}+\mathbf{w}$ often requires use of prior knowledge or structural constraints on $\mathbf{x}$ for accurate reconstruction. Several recent works have considered combining linear least-squares estimation with a generic or "plug-in" denoiser function that can be designed in a modular manner based on the prior knowledge about $\mathbf{x}$. While these methods have shown excellent performance, it has been difficult to obtain rigorous performance guarantees. This work considers plug-in denoising combined with the recently-developed Vector Approximate Message Passing (VAMP) algorithm, which is itself derived via Expectation Propagation techniques. It shown that the mean squared error of this "plug-and-play" VAMP can be exactly predicted for high-dimensional right-rotationally invariant random $\mathbf{A}$ and Lipschitz denoisers. The method is demonstrated on applications in image recovery and parametric bilinear estimation.

Connected through hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "near optimal matrix recovery from random linear measurements"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"

Connected through hyperedge HE-2 (induced by source paper "inference with deep generative priors in high dimensions"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "inverting deep generative models one layer at a time"
    - "finite sample analysis of approximate message passing algorithms"
    - "sunlayer stable denoising with generative networks"
    - "correction by projection denoising images with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "plug and play methods provably converge with properly trained denoisers"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "denoising prior driven deep neural network for image restoration"
    - "regularization by denoising clarifications and new interpretations"
    - "regularized fourier ptychography using an online plug and play algorithm"
    - "lipschitz regularized deep neural networks generalize and are adversarially robust"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"
    - "neural nearest neighbors networks"

Connected through hyperedge HE-4 (induced by source paper "plug and play methods for magnetic resonance imaging"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "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"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-5 (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"
    - "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"
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"

Connected through hyperedge HE-6 (induced by source paper "block coordinate regularization by denoising"):
  with members:
    - "deepred deep image prior powered by red"
    - "regularization by denoising clarifications and new interpretations"
    - "the singular values of convolutional layers"
    - "spectral normalization for generative adversarial networks"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "deep mean shift priors for image restoration"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"

Connected through hyperedge HE-7 (induced by source paper "regularization by denoising clarifications and new interpretations"):
  with members:
    - "local kernels that approximate bayesian regularization and proximal operators"
    - "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"
    - "plug and play unplugged optimization free reconstruction using consensus equilibrium"

Connected through hyperedge HE-8 (induced by source paper "bilinear adaptive generalized vector approximate message passing"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "hybrid approximate message passing"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "parametric bilinear generalized approximate message passing"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "vector approximate message passing for the generalized linear model"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "self calibration and biconvex compressive sensing"

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)