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: "generalized phase retrieval measurement number matrix recovery and beyond"
  Abstract: In this paper, we develop a framework of generalized phase retrieval in which one aims to reconstruct a vector ${\mathbf x}$ in ${\mathbb R}^d$ or ${\mathbb C}^d$ through quadratic samples ${\mathbf x}^*A_1{\mathbf x}, \dots, {\mathbf x}^*A_N{\mathbf x}$. The generalized phase retrieval includes as special cases the standard phase retrieval as well as the phase retrieval by orthogonal projections. We first explore the connections among generalized phase retrieval, low-rank matrix recovery and nonsingular bilinear form. Motivated by the connections, we present results on the minimal measurement number needed for recovering a matrix that lies in a set $W\in {\mathbb C}^{d\times d}$. Applying the results to phase retrieval, we show that generic $d \times d$ matrices $A_1,\ldots, A_N$ have the phase retrieval property if $N\geq 2d-1$ in the real case and $N \geq 4d-4$ in the complex case for very general classes of $A_1,\ldots,A_N$, e.g. matrices with prescribed ranks or orthogonal projections. Our method also leads to a novel proof for the classical Stiefel-Hopf condition on nonsingular bilinear form. We also give lower bounds on the minimal measurement number required for generalized phase retrieval. For several classes of dimensions $d$ we obtain the precise values of the minimal measurement number. Our work unifies and enhances results from the standard phase retrieval, phase retrieval by projections and low-rank matrix recovery.

Connected through hyperedge HE-1 (induced by source paper "solving complex quadratic systems with full rank random matrices"):
  with members:
    - "reconstructing point sets from distance distributions"
    - "linear spectral estimators and an application to phase retrieval"
    - "nonconvex matrix factorization from rank one measurements"
    - "coordinate descent algorithms for phase retrieval"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "stable low rank matrix recovery via null space properties"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-2 (induced by source paper "the estimation performance of nonlinear least squares for phase retrieval"):
  with members:
    - "phase retrieval from the norms of affine transformations"
    - "sparse phase retrieval via truncated amplitude flow"
    - "phasemax convex phase retrieval via basis pursuit"
    - "a geometric analysis of 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"
    - "a strong restricted isometry property with an application to phaseless compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "almost everywhere injectivity conditions for the matrix recovery problem"):
  with members:
    - "on the 2d phase retrieval problem"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "phase transitions in phase retrieval"
    - "low rank matrix recovery from rank one measurements"
    - "rank minimization over finite fields fundamental limits and coding theoretic interpretations"
    - "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 "conjugate phase retrieval in paley wiener space"):
  with members:
    - "phasemax convex phase retrieval via basis pursuit"
    - "phaseless signal recovery in infinite dimensional spaces using structured modulations"

Connected through hyperedge HE-5 (induced by source paper "on dc based methods for phase retrieval"):
  with members:
    - "phase retrieval from the norms of affine transformations"
    - "a geometric analysis of phase retrieval"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval an overview of recent developments"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "phase retrieval stability and recovery guarantees"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"

Connected through hyperedge HE-6 (induced by source paper "phase retrieval from the norms of affine transformations"):
  with members:
    - "phase retrieval with background information"

Connected through hyperedge HE-7 (induced by source paper "almost everywhere matrix recovery"):
  with members:
    - "on the 2d phase retrieval problem"
    - "phase transitions in phase retrieval"
    - "rank minimization over finite fields fundamental limits and coding theoretic interpretations"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-8 (induced by source paper "the minimal measurement number problem in phase retrieval a review of recent developments"):
  with members:
    - "phase retrieval from the magnitudes of affine linear measurements"
    - "compressive phase retrieval via generalized approximate message passing"
    - "phase retrieval for sparse signals"

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)