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: "prime phase retrieval via majorization minimization"
  Abstract: This paper considers the phase retrieval problem in which measurements consist of only the magnitude of several linear measurements of the unknown, e.g., spectral components of a time sequence. We develop low-complexity algorithms with superior performance based on the majorization-minimization (MM) framework. The proposed algorithms are referred to as PRIME: Phase Retrieval vIa the Majorization-minimization techniquE. They are preferred to existing benchmark methods since at each iteration a simple surrogate problem is solved with a closed-form solution that monotonically decreases the original objective function. In total, three algorithms are proposed using different majorization-minimization techniques. Experimental results validate that our algorithms outperform existing methods in terms of successful recovery and mean-square error under various settings.

Connected through hyperedge HE-1 (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-2 (induced by source paper "undersampled sparse phase retrieval via majorization minimization"):
  with members:
    - "scalable solvers of random quadratic equations via stochastic truncated amplitude flow"
    - "compressive phase retrieval via generalized approximate message passing"
    - "sparse phase retrieval from short time fourier measurements"
    - "a strong restricted isometry property with an application to phaseless compressed sensing"
    - "sparse modeling for image and vision processing"
    - "gespar efficient phase retrieval of sparse signals"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-3 (induced by source paper "phase retrieval of real valued signals in a shift invariant space"):
  with members:
    - "phaseless sampling and reconstruction of real valued signals in shift invariant spaces"
    - "phase retrieval from the magnitudes of affine linear measurements"
    - "nonlinear frames and sparse reconstructions in banach spaces"
    - "spatially distributed sampling and reconstruction"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval an overview of recent developments"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "phase retrieval via structured modulations in paley wiener spaces"

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)