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 "a precise analysis of phasemax in phase retrieval"):
  Abstract: Recovering an unknown complex signal from the magnitude of linear combinations of the signal is referred to as phase retrieval. We present an exact performance analysis of a recently proposed convex-optimization-formulation for this problem, known as PhaseMax. Standard convex-relaxation-based methods in phase retrieval resort to the idea of "lifting" which makes them computationally inefficient, since the number of unknowns is effectively squared. In contrast, PhaseMax is a novel convex relaxation that does not increase the number of unknowns. Instead it relies on an initial estimate of the true signal which must be externally provided. In this paper, we investigate the required number of measurements for exact recovery of the signal in the large system limit and when the linear measurement matrix is random with iid standard normal entries. If $n$ denotes the dimension of the unknown complex signal and $m$ the number of phaseless measurements, then in the large system limit, $\frac{m}{n} > \frac{4}{\cos^2(\theta)}$ measurements is necessary and sufficient to recover the signal with high probability, where $\theta$ is the angle between the initial estimate and the true signal. Our result indicates a sharp phase transition in the asymptotic regime which matches the empirical result in numerical simulations.

Includes members:
  Member "phase retrieval via linear programming fundamental limits and algorithmic improvements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using black box compression algorithms for phase retrieval")
  Member "fundamental limits of phasemax for phase retrieval a replica analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using black box compression algorithms for phase retrieval")
  Member "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization")
  Member "blind deconvolution with additional autocorrelations via convex programs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blind phaseless short time fourier transform recovery")
  Member "phasemax convex phase retrieval via basis pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")
  Member "precise error analysis of regularized m estimators in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "precise performance analysis of the box elastic net under matrix uncertainties")
  Member "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "phase retrieval meets statistical learning theory a flexible convex relaxation":
    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)