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: "on fienup methods for sparse phase retrieval"
  Abstract: Alternating minimization, or Fienup methods, have a long history in phase retrieval. We provide new insights related to the empirical and theoretical analysis of these algorithms when used with Fourier measurements and combined with convex priors. In particular, we show that Fienup methods can be viewed as performing alternating minimization on a regularized nonconvex least-squares problem with respect to amplitude measurements. Furthermore, we prove that under mild additional structural assumptions on the prior (semialgebraicity), the sequence of signal estimates has a smooth convergent behavior toward a critical point of the nonconvex regularized least-squares objective. Finally, we propose an extension to Fienup techniques, based on a projected gradient descent interpretation and acceleration using inertial terms. We demonstrate experimentally that this modification combined with an    $\ell _1$   prior constitutes a competitive approach for sparse phase retrieval.

Connected through hyperedge HE-1 (induced by source paper "a note on douglas rachford subgradients and phase retrieval"):
  with members:
    - "fourier phase retrieval uniqueness and algorithms"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "non convex phase retrieval from stft measurements"
    - "the complexity of bit retrieval"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "a geometric analysis of phase retrieval"
    - "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-2 (induced by source paper "deep ptych subsampled fourier ptychography using generative priors"):
  with members:
    - "deep learning approach for fourier ptychography microscopy"
    - "phase retrieval under a generative prior"
    - "robust compressive phase retrieval via deep generative priors"
    - "single image super resolution based on wiener filter in similarity domain"
    - "phase retrieval for fourier ptychography under varying amount of measurements"
    - "progressive growing of gans for improved quality stability and variation"
    - "fourier phase retrieval uniqueness and algorithms"
    - "global guarantees for enforcing deep generative priors by empirical risk"

Connected through hyperedge HE-3 (induced by source paper "fourier phase retrieval uniqueness and algorithms"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "bispectrum inversion with application to multireference alignment"
    - "on signal reconstruction from frog measurements"
    - "on the 2d phase retrieval problem"
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "on the uniqueness of frog methods"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "multireference alignment is easier with an aperiodic translation distribution"

Connected through hyperedge HE-4 (induced by source paper "non convex phase retrieval from stft measurements"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "bispectrum inversion with application to multireference alignment"
    - "on signal reconstruction from frog measurements"
    - "fourier phase retrieval uniqueness and algorithms"
    - "on the uniqueness of frog methods"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"

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)