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 "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"):
  Abstract: This paper concerns the problem of recovering an unknown but structured signal $x \in R^n$ from $m$ quadratic measurements of the form $y_r=| |^2$ for $r=1,2,...,m$. We focus on the under-determined setting where the number of measurements is significantly smaller than the dimension of the signal ($m<<n$). We formulate the recovery problem as a nonconvex optimization problem where prior structural information about the signal is enforced through constrains on the optimization variables. We prove that projected gradient descent, when initialized in a neighborhood of the desired signal, converges to the unknown signal at a linear rate. These results hold for any constraint set (convex or nonconvex) providing convergence guarantees to the global optimum even when the objective function and constraint set is nonconvex. Furthermore, these results hold with a number of measurements that is only a constant factor away from the minimal number of measurements required to uniquely identify the unknown signal. Our results provide the first provably tractable algorithm for this data-poor regime, breaking local sample complexity barriers that have emerged in recent literature. In a companion paper we demonstrate favorable properties for the optimization problem that may enable similar results to continue to hold more globally (over the entire ambient space). Collectively these two papers utilize and develop powerful tools for uniform convergence of empirical processes that may have broader implications for rigorous understanding of constrained nonconvex optimization heuristics. The mathematical results in this paper also pave the way for a new generation of data-driven phase-less imaging systems that can utilize prior information to significantly reduce acquisition time and enhance image reconstruction, enabling nano-scale imaging at unprecedented speeds and resolutions.

Includes members:
  Member "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a precise analysis of phasemax in phase retrieval")
  Member "improved recovery guarantees for phase retrieval from coded diffraction patterns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "complex phase retrieval from subgaussian measurements")
  Member "sparse phase retrieval via truncated amplitude flow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable low rank phase retrieval and compressive pca")
  Member "non convex phase retrieval from stft measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "toward a mathematical theory of the crystallographic phase retrieval problem")
  Member "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "noncoherent short packet communication via modulation on conjugated zeros")
  Member "stft phase retrieval uniqueness guarantees and recovery algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "toward a mathematical theory of the crystallographic phase retrieval problem")
  Member "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "solving equations of random convex functions via anchored regression")
  Member "phasemax convex phase retrieval via basis pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")

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.LG(Machine Learning)