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 "solving complex quadratic systems with full rank random matrices"):
  Abstract: We tackle the problem of recovering a complex signal $\boldsymbol x\in\mathbb{C}^n$ from quadratic measurements of the form $y_i=\boldsymbol x^*\boldsymbol A_i\boldsymbol x$, where $\{\boldsymbol A_i\}_{i=1}^m$ is a set of full-rank, complex random matrices with rotation-invariant entries. We formulate it as the minimization of a nonconvex loss. This problem is related to the well understood phase retrieval where $\boldsymbol A_i$ is a rank-1 positive semidefinite matrix. Here we study the general full-rank case which models a number of key applications such as molecular geometry recovery from distance distributions and compound measurements in phaseless diffractive imaging. Most prior work either addresses the rank-1 case or focuses on real measurements. The several papers that address the full-rank complex case adopt the computationally-demanding semidefinite relaxation approach. In this paper we prove that the general class of problems with rotation-invariant sub-Gaussian measurement models can be efficiently solved with high probability via the standard framework comprising a spectral initialization followed by iterative gradient descent updates on a nonconvex loss. Numerical experiments on simulated data corroborate our theoretical analysis.

Includes members:
  Member "reconstructing point sets from distance distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "permutations unlabeled beyond sampling unknown")
  Member "linear spectral estimators and an application to phase retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "nonconvex matrix factorization from rank one measurements":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning one hidden layer neural networks via provable gradient descent with random initialization")
  Member "coordinate descent algorithms for phase retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimization based amp for phase retrieval the impact of initialization and ell_2 regularization")
  Member "phase transitions of spectral initialization for high dimensional nonconvex estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "generalized phase retrieval measurement number matrix recovery and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the estimation performance of nonlinear least squares for phase retrieval")
  Member "stable low rank matrix recovery via null space properties":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "solving random quadratic systems of equations is nearly as easy as solving linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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)