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 using feasible point pursuit algorithms and cramer rao bound"
  Abstract: Reconstructing a signal from squared linear (rank-1 quadratic) measurements is a challenging problem with important applications in optics and imaging, where it is known as  phase retrieval . This paper proposes two new phase retrieval algorithms based on nonconvex quadratically constrained quadratic programming) formulations, and a recently proposed approximation technique dubbed  feasible point pursuit (FPP) . The first is designed for uniformly distributed bounded measurement errors, such as those arising from high-rate quantization (B-FPP). The second is designed for Gaussian measurement errors, using a least-squares criterion (LS-FPP). Their performance is measured against state-of-the-art algorithms and the Cramer–Rao bound (CRB), which is also derived here. Simulations show that LS-FPP outperforms the existing schemes and operates close to the CRB. Compact CRB expressions, properties, and insights are obtained by explicitly computing the CRB in various special cases—including when the signal of interest admits a sparse parametrization, using harmonic retrieval as an example.

Connected through hyperedge HE-1 (induced by source paper "phase retrieval from binary measurements"):
  with members:
    - "undersampled sparse phase retrieval via majorization minimization"
    - "low rank phase retrieval"
    - "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 "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"
    - "phasemax convex phase retrieval via basis pursuit"
    - "guaranteed matrix completion via non convex factorization"
    - "prime phase retrieval via majorization minimization"

Connected through hyperedge HE-3 (induced by source paper "inexact alternating optimization for phase retrieval in the presence of outliers"):
  with members:
    - "scalable solvers of random quadratic equations via stochastic truncated amplitude flow"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "parallel matrix factorization for low rank tensor completion"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

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"
    - "undersampled sparse phase retrieval via majorization minimization"
    - "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"

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)