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: "gespar efficient phase retrieval of sparse signals"
  Abstract: We consider the problem of phase retrieval, namely, recovery of a signal from the magnitude of its Fourier transform, or of any other linear transform. Due to the loss of the Fourier phase information, this problem is ill-posed. Therefore, prior information on the signal is needed in order to enable its recovery. In this work we consider the case in which the signal is known to be sparse, i.e., it consists of a small number of nonzero elements in an appropriate basis. We propose a fast local search method for recovering a sparse signal from measurements of its Fourier transform (or other linear transform) magnitude which we refer to as GESPAR: GrEedy Sparse PhAse Retrieval. Our algorithm does not require matrix lifting, unlike previous approaches, and therefore is potentially suitable for large scale problems such as images. Simulation results indicate that GESPAR is fast and more accurate than existing techniques in a variety of settings.

Connected through hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "information theoretic and algorithmic thresholds for group testing"
    - "efficient data collection over multiple access wireless sensors network"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "distributed compressed sensing for static and time varying networks"

Connected through hyperedge HE-2 (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"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"

Connected through hyperedge HE-3 (induced by source paper "serial quantization for representing sparse signals"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "hardware limited task based quantization"
    - "efficient data collection over multiple access wireless sensors network"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "spatial compressive sensing for mimo radar"
    - "group testing algorithms bounds and simulations"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "phase retrieval from binary measurements"):
  with members:
    - "undersampled sparse phase retrieval via majorization minimization"
    - "low rank phase retrieval"
    - "phase retrieval using feasible point pursuit algorithms and cramer rao bound"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "the generalized lasso with non linear observations"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-5 (induced by source paper "phaseless subspace tracking"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "recovery of sparse 1 d signals from the magnitudes of their fourier transform"

Connected through hyperedge HE-6 (induced by source paper "super resolution phase retrieval for sparse signals"):
  with members:
    - "sparse phase retrieval from short time fourier measurements"
    - "phase retrieval for sparse signals uniqueness conditions"
    - "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-7 (induced by source paper "on fienup methods for sparse phase retrieval"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "on conditions for uniqueness in sparse phase retrieval"
    - "recovery of sparse 1 d signals from the magnitudes of their fourier transform"
    - "phase retrieval via matrix completion"
    - "sparsity based sub wavelength imaging with partially incoherent light via quadratic compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "global optimization of spin hamiltonians with gain dissipative systems"):
  with members:
    - "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)