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: "efficient compressive phase retrieval with constrained sensing vectors"
  Abstract: We propose a robust and efficient approach to the problem of compressive phase retrieval in which the goal is to reconstruct a sparse vector from the magnitude of a number of its linear measurements. The proposed framework relies on constrained sensing vectors and a two-stage reconstruction method that consists of two standard convex programs that are solved sequentially. #R##N#In recent years, various methods are proposed for compressive phase retrieval, but they have suboptimal sample complexity or lack robustness guarantees. The main obstacle has been that there is no straightforward convex relaxations for the type of structure in the target. Given a set of underdetermined measurements, there is a standard framework for recovering a sparse matrix, and a standard framework for recovering a low-rank matrix. However, a general, efficient method for recovering a jointly sparse and low-rank matrix has remained elusive. #R##N#Deviating from the models with generic measurements, in this paper we show that if the sensing vectors are chosen at random from an incoherent subspace, then the low-rank and sparse structures of the target signal can be effectively decoupled. We show that a recovery algorithm that consists of a low-rank recovery stage followed by a sparse recovery stage will produce an accurate estimate of the target when the number of measurements is $\mathsf{O}(k\,\log\frac{d}{k})$, where $k$ and $d$ denote the sparsity level and the dimension of the input signal. We also evaluate the algorithm through numerical simulation.

Connected through hyperedge HE-1 (induced by source paper "alternating phase projected gradient descent with generative priors for solving compressive phase retrieval"):
  with members:
    - "phase retrieval under a generative prior"
    - "robust compressive phase retrieval via deep generative priors"
    - "began boundary equilibrium generative adversarial networks"
    - "image restoration by iterative denoising and backward projections"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "learning deep cnn denoiser prior for image restoration"
    - "sparse phase retrieval via truncated amplitude flow"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "a precise analysis of phasemax in phase retrieval"):
  with members:
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "blind deconvolution with additional autocorrelations via convex programs"
    - "phasemax convex phase retrieval via basis pursuit"
    - "precise error analysis of regularized m estimators in high dimensions"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"

Connected through hyperedge HE-3 (induced by source paper "phase retrieval under a generative prior"):
  with members:
    - "sparse phase retrieval via truncated amplitude flow"
    - "a geometric analysis of phase retrieval"
    - "why are deep nets reversible a simple theory with implications for training"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"
    - "sparse phase retrieval convex algorithms and limitations"
    - "sparse signal recovery from quadratic measurements via convex programming"

Connected through hyperedge HE-4 (induced by source paper "phase retrieval for signals in union of subspaces"):
  with members:
    - "phasemax convex phase retrieval via basis pursuit"
    - "phase retrieval from coded diffraction patterns"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"):
  with members:
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "sparse phase retrieval via truncated amplitude flow"
    - "non convex phase retrieval from stft measurements"
    - "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space"
    - "phasemax convex phase retrieval via basis pursuit"

Connected through hyperedge HE-6 (induced by source paper "millimeter wave channel estimation via exploiting joint sparse and low rank structures"):
  with members:
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "low rf complexity technologies to enable millimeter wave mimo with large antenna array for 5g wireless communications"
    - "low rank tensor decomposition aided channel estimation for millimeter wave mimo ofdm systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-7 (induced by source paper "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"):
  with members:
    - "phase retrieval an overview of recent developments"
    - "phase retrieval with application to optical imaging"
    - "phase retrieval from coded diffraction patterns"
    - "sparse phase retrieval convex algorithms and limitations"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-8 (induced by source paper "solving random quadratic systems of equations is nearly as easy as solving linear systems"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "guaranteed matrix completion via non convex factorization"
    - "simple efficient and neural algorithms for sparse coding"
    - "a partial derandomization of phaselift using spherical designs"
    - "compressive phase retrieval via generalized approximate message passing"
    - "complete dictionary recovery over the sphere"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "near optimal joint object matching via convex relaxation"

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)