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: "optimization based amp for phase retrieval the impact of initialization and ell_2 regularization"
  Abstract: We consider an $\ell_2$-regularized non-convex optimization problem for recovering signals from their noisy phaseless observations. We design and study the performance of a message passing algorithm that aims to solve this optimization problem. We consider the asymptotic setting $m,n \rightarrow \infty$, $m/n \rightarrow \delta$ and obtain sharp performance bounds, where $m$ is the number of measurements and $n$ is the signal dimension. We show that for complex signals the algorithm can perform accurate recovery with only $m= \frac{64}{\pi^2}-4 \approx 2.5n$ measurements. Also, we provide sharp analysis on the sensitivity of the algorithm to noise. We highlight the following facts about our message passing algorithm: (i) Adding $\ell_2$ regularization to the non-convex loss function can be beneficial. (ii) Spectral initialization has marginal impact on the performance of the algorithm. The sharp analyses in this paper, not only enable us to compare the performance of our method with other phase recovery schemes, but also shed light on designing better iterative algorithms for other non-convex optimization problems.

Connected through hyperedge HE-1 (induced by source paper "spectral method for phase retrieval an expectation propagation perspective"):
  with members:
    - "optimal spectral initialization for signal recovery with applications to phase retrieval"
    - "expectation propagation for approximate inference free probability framework"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "expectation consistent approximate inference generalizations and convergence"
    - "vector approximate message passing for the generalized linear model"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"

Connected through hyperedge HE-2 (induced by source paper "matrix calibration based cascaded channel estimation for reconfigurable intelligent surface assisted multiuser mimo"):
  with members:
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "passive beamforming and information transfer via large intelligent surface"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "performance analysis of large intelligent surfaces liss uplink spectral efficiency and pilot training"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "large intelligent surface assisted wireless communication exploiting statistical csi"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"

Connected through hyperedge HE-3 (induced by source paper "optimal spectral initialization for signal recovery with applications to phase retrieval"):
  with members:
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "sparse phase retrieval convex algorithms and limitations"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-4 (induced by source paper "nonconvex matrix factorization from rank one measurements"):
  with members:
    - "recovery guarantees for one hidden layer neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "low rank positive semidefinite matrix recovery from corrupted rank one measurements"
    - "a geometric analysis of phase retrieval"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "low rank matrix recovery from rank one 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)