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: "non convex phase retrieval from stft measurements"
  Abstract: The problem of recovering a one-dimensional signal from its Fourier transform magnitude, called Fourier phase retrieval, is ill-posed in most cases. We consider the closely-related problem of recovering a signal from its phaseless short-time Fourier transform (STFT) measurements. This problem arises naturally in several applications, such as ultra-short laser pulse characterization and ptychography. The redundancy offered by the STFT enables unique recovery under mild conditions. We show that in some cases the unique solution can be obtained by the principal eigenvector of a matrix, constructed as the solution of a simple least-squares problem. When these conditions are not met, we suggest using the principal eigenvector of this matrix to initialize non-convex local optimization algorithms and propose two such methods. The first is based on minimizing the empirical risk loss function, while the second maximizes a quadratic function on the manifold of phases. We prove that under appropriate conditions, the proposed initialization is close to the underlying signal. We then analyze the geometry of the empirical risk loss function and show numerically that both gradient algorithms converge to the underlying signal even with small redundancy in the measurements. In addition, the algorithms are robust to noise.

Connected through hyperedge HE-1 (induced by source paper "toward a mathematical theory of the crystallographic phase retrieval problem"):
  with members:
    - "a note on douglas rachford subgradients and phase retrieval"
    - "phase retrieval for binary signals box relaxation and uniqueness"
    - "learning without loss"
    - "single particle cryo electron microscopy mathematical theory computational challenges and opportunities"
    - "blind phaseless short time fourier transform recovery"
    - "on signal reconstruction from frog measurements"
    - "fourier phase retrieval uniqueness and algorithms"
    - "on the uniqueness of frog methods"

Connected through hyperedge HE-2 (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"
    - "linear spectral estimators and an application 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"
    - "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-3 (induced by source paper "a note on douglas rachford subgradients and phase retrieval"):
  with members:
    - "on fienup methods for sparse phase retrieval"
    - "fourier phase retrieval uniqueness and algorithms"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "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-4 (induced by source paper "blind phaseless short time fourier transform recovery"):
  with members:
    - "multi target detection with application to cryo electron microscopy"
    - "toward single particle reconstruction without particle picking breaking the detection limit"
    - "blind deconvolutional phase retrieval via convex programming"
    - "bispectrum inversion with application to multireference alignment"
    - "on signal reconstruction from frog measurements"
    - "stable blind deconvolution over the reals from additional autocorrelations"
    - "fourier phase retrieval uniqueness and algorithms"
    - "on the uniqueness of frog methods"

Connected through hyperedge HE-5 (induced by source paper "measurement matrix design for phase retrieval based on mutual information"):
  with members:
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "intensity only optical compressive imaging using a multiply scattering material and a double phase retrieval approach"
    - "almost lossless analog compression without phase information"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "phase retrieval from coded diffraction patterns"
    - "phase retrieval stability and recovery guarantees"
    - "communications inspired projection design with application to compressive sensing"

Connected through hyperedge HE-6 (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"
    - "on fienup methods for sparse phase retrieval"
    - "progressive growing of gans for improved quality stability and variation"
    - "fourier phase retrieval uniqueness and algorithms"

Connected through hyperedge HE-7 (induced by source paper "bispectrum inversion with application to multireference alignment"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "on signal reconstruction from frog measurements"
    - "fourier phase retrieval uniqueness and algorithms"
    - "on the uniqueness of frog methods"
    - "the sample complexity of multi reference alignment"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "fundamental limits in multi image alignment"
    - "near optimal joint object matching via convex relaxation"

Connected through hyperedge HE-8 (induced by source paper "linear spectral estimators and an application to phase retrieval"):
  with members:
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "phase retrieval via matrix completion"

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)