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: "low rank phase retrieval"
  Abstract: We develop two iterative algorithms for solving the low-rank phase retrieval (LRPR) problem. LRPR refers to recovering a low-rank matrix   $\boldsymbol {X}$   from magnitude-only (phaseless) measurements of random linear projections of its columns. Both methods consist of a spectral initialization step followed by an iterative algorithm to maximize the observed data likelihood. We obtain sample complexity bounds for our proposed initialization approach to provide a good approximation of the true   $\boldsymbol {X}$  . When the rank is low enough, these bounds are significantly lower than what existing single vector phase retrieval algorithms need. Via extensive experiments, we show that the same is also true for the proposed complete algorithms.

Connected through hyperedge HE-1 (induced by source paper "phase retrieval of low rank matrices by anchored regression"):
  with members:
    - "blind phaseless short time fourier transform recovery"
    - "blind deconvolutional phase retrieval via convex programming"
    - "blind gain and phase calibration via sparse spectral methods"
    - "solving equations of random convex functions via anchored regression"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "self calibration via linear least squares"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"

Connected through hyperedge HE-2 (induced by source paper "provable low rank phase retrieval and compressive pca"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "provable subspace tracking from missing data and matrix completion"
    - "phaseless pca low rank matrix recovery from column wise phaseless measurements"
    - "provable dynamic robust pca or robust subspace tracking"
    - "nearly optimal robust subspace tracking"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "sparse phase retrieval via truncated amplitude flow"
    - "fast algorithms for robust pca via gradient descent"

Connected through hyperedge HE-3 (induced by source paper "phaseless pca low rank matrix recovery from column wise phaseless measurements"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "phaseless subspace tracking"
    - "nearly optimal robust subspace tracking"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "subspace learning with partial information"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"
    - "support predicted modified cs for recursive robust principal components pursuit"

Connected through hyperedge HE-4 (induced by source paper "spectral algorithm for low rank multitask regression"):
  with members:
    - "solving complex quadratic systems with full rank random matrices"
    - "learning to multitask"
    - "a survey on multi task learning"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "very deep convolutional networks for large scale image recognition"
    - "a model of inductive bias learning"

Connected through hyperedge HE-5 (induced by source paper "static and dynamic robust pca and matrix completion a review"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "an overview of robust subspace recovery"
    - "video denoising via dynamic video layering"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"
    - "correlated pca principal components analysis when data and noise are correlated"

Connected through hyperedge HE-6 (induced by source paper "static and dynamic robust pca via low rank sparse matrix decomposition a review"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "video denoising via dynamic video layering"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"
    - "correlated pca principal components analysis when data and noise are correlated"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-7 (induced by source paper "phase retrieval from binary measurements"):
  with members:
    - "undersampled sparse phase retrieval via majorization minimization"
    - "phase retrieval using feasible point pursuit algorithms and cramer rao bound"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "the generalized lasso with non linear observations"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "gespar efficient phase retrieval of sparse signals"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-8 (induced by source paper "quantization aware phase retrieval"):
  with members:
    - "phase retrieval from binary measurements"
    - "undersampled sparse phase retrieval via majorization minimization"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"
    - "sparsity based sub wavelength imaging with partially incoherent light via quadratic compressed sensing"
    - "one bit compressed sensing by linear programming"

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)