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: "learning and free energies for vector approximate message passing"
  Abstract: Vector approximate message passing (VAMP) is a computationally simple approach to the recovery of a signal $\mathbf{x}$ from noisy linear measurements $\mathbf{y}=\mathbf{Ax}+\mathbf{w}$. Like the AMP proposed by Donoho, Maleki, and Montanari in 2009, VAMP is characterized by a rigorous state evolution (SE) that holds under certain large random matrices and that matches the replica prediction of optimality. But while AMP's SE holds only for large i.i.d. sub-Gaussian $\mathbf{A}$, VAMP's SE holds under the much larger class: right-rotationally invariant $\mathbf{A}$. To run VAMP, however, one must specify the statistical parameters of the signal and noise. This work combines VAMP with Expectation-Maximization to yield an algorithm, EM-VAMP, that can jointly recover $\mathbf{x}$ while learning those statistical parameters. The fixed points of the proposed EM-VAMP algorithm are shown to be stationary points of a certain constrained free-energy, providing a variational interpretation of the algorithm. Numerical simulations show that EM-VAMP is robust to highly ill-conditioned $\mathbf{A}$ with performance nearly matching oracle-parameter VAMP.

Connected through hyperedge HE-1 (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"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "sunlayer stable denoising with generative networks"

Connected through hyperedge HE-2 (induced by source paper "an expectation maximization approach to tuning generalized vector approximate message passing"):
  with members:
    - "hybrid approximate message passing"
    - "expectation consistent approximate inference generalizations and convergence"
    - "vector approximate message passing for the generalized linear model"
    - "phase recovery from a bayesian point of view the variational approach"
    - "compressive phase retrieval via generalized approximate message passing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "hybrid approximate message passing"

Connected through hyperedge HE-3 (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"
    - "parametric bilinear generalized approximate message passing"

Connected through hyperedge HE-4 (induced by source paper "amp inspired deep networks for sparse linear inverse problems"):
  with members:
    - "multi layer generalized linear estimation"
    - "hybrid approximate message passing"
    - "deep fully connected networks for video compressive sensing"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "vector approximate message passing for the generalized linear model"

Connected through hyperedge HE-5 (induced by source paper "inference in deep networks in high dimensions"):
  with members:
    - "multi layer generalized linear estimation"
    - "hybrid approximate message passing"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"
    - "inferring sparsity compressed sensing using generalized restricted boltzmann machines"
    - "vector approximate message passing for the generalized linear model"
    - "semantic image inpainting with deep generative models"

Connected through hyperedge HE-6 (induced by source paper "channel estimation in broadband millimeter wave mimo systems with few bit adcs"):
  with members:
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "massive mimo with low resolution adcs"
    - "vector approximate message passing for the generalized linear model"
    - "performance of the wideband massive uplink mimo with one bit adcs"
    - "mixed adc massive mimo detectors performance analysis and design optimization"
    - "one bit massive mimo channel estimation and high order modulations"

Connected through hyperedge HE-7 (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"
    - "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"
    - "on the convergence of approximate message passing with arbitrary matrices"

Connected through hyperedge HE-8 (induced by source paper "denoising based vector approximate message passing"):
  with members:
    - "hybrid approximate message passing"
    - "from denoising to compressed sensing"
    - "hybrid approximate message passing"
    - "message passing algorithms for compressed 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)