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: "linear spectral estimators and an application to phase retrieval"
  Abstract: Phase retrieval refers to the problem of recovering real- or complex-valued vectors from magnitude measurements. The best-known algorithms for this problem are iterative in nature and rely on so-called spectral initializers that provide accurate initialization vectors. We propose a novel class of estimators suitable for general nonlinear measurement systems, called linear spectral estimators (LSPEs), which can be used to compute accurate initialization vectors for phase retrieval problems. The proposed LSPEs not only provide accurate initialization vectors for noisy phase retrieval systems with structured or random measurement matrices, but also enable the derivation of sharp and nonasymptotic mean-squared error bounds. We demonstrate the efficacy of LSPEs on synthetic and real-world phase retrieval problems, and show that our estimators significantly outperform existing methods for structured measurement systems that arise in practice.

Connected through hyperedge HE-1 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "optimal spectral initialization for signal recovery with applications to phase retrieval"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "non convex phase retrieval from stft measurements"
    - "phasemax convex phase retrieval via basis pursuit"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"

Connected through hyperedge HE-2 (induced by source paper "using black box compression algorithms for phase retrieval"):
  with members:
    - "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"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-3 (induced by source paper "solving complex quadratic systems with full rank random matrices"):
  with members:
    - "reconstructing point sets from distance distributions"
    - "nonconvex matrix factorization from rank one measurements"
    - "coordinate descent algorithms for phase retrieval"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "generalized phase retrieval measurement number matrix recovery and beyond"
    - "stable low rank matrix recovery via null space properties"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-4 (induced by source paper "compressive phase retrieval of structured signal"):
  with members:
    - "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"
    - "restoration by compression"
    - "the little engine that could regularization by denoising red"
    - "phasemax convex phase retrieval via basis pursuit"
    - "compression based compressed sensing"
    - "phase retrieval meets statistical learning theory a flexible 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)