human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "scalable solvers of random quadratic equations via stochastic truncated amplitude flow":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressive phase retrieval via reweighted amplitude flow")
  Member "prime phase retrieval via majorization minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable solvers of random quadratic equations via stochastic truncated amplitude flow")
  Member "compressive phase retrieval via generalized approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "sparse phase retrieval from short time fourier measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "a strong restricted isometry property with an application to phaseless compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the estimation performance of nonlinear least squares for phase retrieval")
  Member "sparse modeling for image and vision processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "gespar efficient phase retrieval of sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "serial quantization for sparse time sequences")
  Member "sparse signal recovery from quadratic measurements via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "phase retrieval of low rank matrices by anchored regression")

Question: Please tell me which class the source paper 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)