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: "phase retrieval from the norms of affine transformations"
  Abstract: In this paper, we consider the generalized phase retrieval from affine measurements. This problem aims to recover signals ${\mathbf x} \in {\mathbb F}^d$ from the affine measurements $y_j=\norm{M_j^*\vx +{\mathbb b}_j}^2,\; j=1,\ldots,m,$ where $M_j \in {\mathbb F}^{d\times r}, {\mathbf b}_j\in {\mathbb F}^{r}, {\mathbb F}\in \{{\mathbb R},{\mathbb C}\}$ and we call it as {\em generalized affine phase retrieval}. We develop a framework for generalized affine phase retrieval with presenting necessary and sufficient conditions for $\{(M_j,{\mathbf b}_j)\}_{j=1}^m$ having generalized affine phase retrieval property. We also establish results on minimal measurement number for generalized affine phase retrieval. Particularly, we show if $\{(M_j,{\mathbf b}_j)\}_{j=1}^m \subset {\mathbb F}^{d\times r}\times {\mathbb F}^{r}$ has generalized affine phase retrieval property, then $m\geq d+\floor{d/r}$ for ${\mathbb F}={\mathbb R}$ ($m\geq 2d+\floor{d/r}$ for ${\mathbb F}={\mathbb C}$ ). We also show that the bound is tight provided $r\mid d$. These results imply that one can reduce the measurement number by raising $r$, i.e. the rank of $M_j$. This highlights a notable difference between generalized affine phase retrieval and generalized phase retrieval. Furthermore, using tools of algebraic geometry, we show that $m\geq 2d$ (resp. $m\geq 4d-1$) generic measurements ${\mathcal A}=\{(M_j,b_j)\}_{j=1}^m$ have the generalized phase retrieval property for ${\mathbb F}={\mathbb R}$ (resp. ${\mathbb F}={\mathbb C}$).

Connected through hyperedge HE-1 (induced by source paper "the estimation performance of nonlinear least squares for phase retrieval"):
  with members:
    - "sparse phase retrieval via truncated amplitude flow"
    - "generalized phase retrieval measurement number matrix recovery and beyond"
    - "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-2 (induced by source paper "on dc based methods for phase retrieval"):
  with members:
    - "generalized phase retrieval measurement number matrix recovery and beyond"
    - "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"

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)