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 for sparse signals uniqueness conditions"
  Abstract: In a variety of fields, in particular those involving imaging and optics, we often measure signals whose phase is missing or has been irremediably distorted. Phase retrieval attempts the recovery of the phase information of a signal from the magnitude of its Fourier transform to enable the reconstruction of the original signal. A fundamental question then is: "Under which conditions can we uniquely recover the signal of interest from its measured magnitudes?" In this paper, we assume the measured signal to be sparse. This is a natural assumption in many applications, such as X-ray crystallography, speckle imaging and blind channel estimation. In this work, we derive a sufficient condition for the uniqueness of the solution of the phase retrieval (PR) problem for both discrete and continuous domains, and for one and multi-dimensional domains. More precisely, we show that there is a strong connection between PR and the turnpike problem, a classic combinatorial problem. We also prove that the existence of collisions in the autocorrelation function of the signal may preclude the uniqueness of the solution of PR. Then, assuming the absence of collisions, we prove that the solution is almost surely unique on 1-dimensional domains. Finally, we extend this result to multi-dimensional signals by solving a set of 1-dimensional problems. We show that the solution of the multi-dimensional problem is unique when the autocorrelation function has no collisions, significantly improving upon a previously known result.

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 "solving complex quadratic systems with full rank random matrices"):
  with members:
    - "reconstructing point sets from distance distributions"
    - "linear spectral estimators and an application to phase retrieval"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "phase retrieval for binary signals box relaxation and uniqueness"):
  with members:
    - "on signal reconstruction from frog measurements"
    - "fourier phase retrieval uniqueness and algorithms"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "on conditions for uniqueness in sparse phase retrieval"
    - "sparse phase retrieval convex algorithms and limitations"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "reconstruction of binary functions and shapes from incomplete frequency information"

Connected through hyperedge HE-4 (induced by source paper "super resolution phase retrieval for sparse signals"):
  with members:
    - "sparse phase retrieval from short time fourier measurements"
    - "gespar efficient phase retrieval of sparse signals"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "fast and robust parametric estimation of jointly sparse channels"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-5 (induced by source paper "phase retrieval with background information"):
  with members:
    - "sparse phase retrieval from short time fourier measurements"
    - "on conditions for uniqueness in sparse phase retrieval"

Connected through hyperedge HE-6 (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 fienup methods for sparse phase retrieval"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "scalable solvers of random quadratic equations via stochastic truncated amplitude flow"):
  with members:
    - "undersampled sparse phase retrieval via majorization minimization"
    - "low rank phase retrieval"
    - "inexact alternating optimization for phase retrieval in the presence of outliers"
    - "sparse phase retrieval via truncated amplitude flow"
    - "non convex phase retrieval from stft measurements"
    - "phase retrieval using feasible point pursuit algorithms and cramer rao bound"
    - "phasemax convex phase retrieval via basis pursuit"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-8 (induced by source paper "on the uniqueness of frog methods"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "non convex phase retrieval from stft measurements"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval an overview of recent developments"
    - "sparse phase retrieval from short time fourier measurements"
    - "gespar efficient phase retrieval of sparse signals"
    - "phase retrieval from coded diffraction patterns"

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)