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: "phase retrieval meets statistical learning theory a flexible convex relaxation"
  Abstract: We propose a flexible convex relaxation for the phase retrieval problem that operates in the natural domain of the signal. Therefore, we avoid the prohibitive computational cost associated with "lifting" and semidefinite programming (SDP) in methods such as PhaseLift and compete with recently developed non-convex techniques for phase retrieval. We relax the quadratic equations for phaseless measurements to inequality constraints each of which representing a symmetric "slab". Through a simple convex program, our proposed estimator finds an extreme point of the intersection of these slabs that is best aligned with a given anchor vector. We characterize geometric conditions that certify success of the proposed estimator. Furthermore, using classic results in statistical learning theory, we show that for random measurements the geometric certificates hold with high probability at an optimal sample complexity. Phase transition of our estimator is evaluated through simulations. Our numerical experiments also suggest that the proposed method can solve phase retrieval problems with coded diffraction measurements as well.

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"
    - "low rank phase retrieval"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "self calibration via linear least squares"

Connected through hyperedge HE-2 (induced by source paper "complex phase retrieval from subgaussian measurements"):
  with members:
    - "on the convex geometry of blind deconvolution and matrix completion"
    - "phase retrieval without small ball probability assumptions"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "phasemax convex phase retrieval via basis pursuit"
    - "a geometric analysis of phase retrieval"
    - "stable low rank matrix recovery via null space properties"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-3 (induced by source paper "sharp restricted isometry bounds for the inexistence of spurious local minima in nonconvex matrix recovery"):
  with members:
    - "how much restricted isometry is needed in nonconvex matrix recovery"
    - "global optimality in low rank matrix optimization"
    - "how to escape saddle points efficiently"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "phasemax convex phase retrieval via basis pursuit"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-4 (induced by source paper "using black box compression algorithms for phase retrieval"):
  with members:
    - "linear spectral estimators and an application to phase retrieval"
    - "robust compressive phase retrieval via deep generative priors"
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "the little engine that could regularization by denoising red"
    - "phasemax convex phase retrieval via basis pursuit"
    - "compression based compressed sensing"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-5 (induced by source paper "simultaneous phase retrieval and blind deconvolution via convex programming"):
  with members:
    - "blind deconvolutional phase retrieval via convex programming"
    - "anchored regression solving random convex equations via convex programming"
    - "benchmark problems for phase retrieval"
    - "phasemax convex phase retrieval via basis pursuit"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "phase retrieval from coded diffraction patterns"
    - "blind deconvolution using convex programming"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-6 (induced by source paper "optimization based amp for phase retrieval the impact of initialization and ell_2 regularization"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "a precise analysis of phasemax in phase retrieval"
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "coordinate descent algorithms for phase retrieval"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"

Connected through hyperedge HE-7 (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"
    - "ber analysis of the box relaxation for bpsk signal recovery"

Connected through hyperedge HE-8 (induced by source paper "towards the optimal construction of a loss function without spurious local minima for solving quadratic equations"):
  with members:
    - "the non convex geometry of low rank matrix optimization"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "on the optimization landscape of tensor decompositions"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "phasemax convex phase retrieval via basis pursuit"
    - "matrix completion has no spurious local minimum"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "a geometric analysis of phase retrieval"

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)