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: "undersampled sparse phase retrieval via majorization minimization"
  Abstract: In the undersampled phase retrieval problem, the goal is to recover an    $N$  -dimensional complex-valued signal from only    $M   intensity measurements without phase information. This inverse system is not only nonconvex, but also underdetermined. In this paper, we propose to exploit the sparsity in the original signal and develop two low-complexity algorithms with superior performance based on the majorization–minimization framework. The proposed algorithms are preferred to existing benchmark methods, since at each iteration a simple convex surrogate problem is solved with a closed-form solution that monotonically decreases the objective function value. When the unknown signal is sparse in the standard basis, the first algorithm C-PRIME can produce a stationary point of the corresponding nonconvex phase retrieval problem. When the unknown signal is not sparse in the standard basis, the second algorithm SC-PRIME can find a coordinatewise stationary point of the more challenging phase retrieval problem through sparse coding. Experimental results validate that the proposed algorithms have higher successful recovery rate and less normalized mean square error than existing up-to-date methods under the same setting.

Connected through hyperedge HE-1 (induced by source paper "phase retrieval from binary measurements"):
  with members:
    - "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"
    - "gespar efficient phase retrieval of sparse signals"
    - "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-2 (induced by source paper "quantization aware phase retrieval"):
  with members:
    - "phase retrieval from binary measurements"
    - "low rank phase retrieval"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"
    - "sparsity based sub wavelength imaging with partially incoherent light via quadratic compressed sensing"
    - "one bit compressed sensing by linear programming"

Connected through hyperedge HE-3 (induced by source paper "scalable solvers of random quadratic equations via stochastic truncated amplitude flow"):
  with members:
    - "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"
    - "prime phase retrieval via majorization minimization"

Connected through hyperedge HE-4 (induced by source paper "sparse phase retrieval via truncated amplitude flow"):
  with members:
    - "channels with cooperation links that may be absent"
    - "sublinear time algorithms for compressive phase retrieval"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "scalable solvers of random quadratic equations via stochastic truncated amplitude flow"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "low rank positive semidefinite matrix recovery from corrupted rank one measurements"
    - "non convex phase retrieval from stft measurements"
    - "phase retrieval using feasible point pursuit algorithms and cramer rao bound"

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)