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: "corruption robust phase retrieval via linear programming"
  Abstract: We consider the problem of phase retrieval from corrupted magnitude observations. In particular we show that a fixed $x_0 \in \mathbb{R}^n$ can be recovered exactly from corrupted magnitude measurements $|\langle a_i, x_0 \rangle | + \eta_i, \quad i =1,2\ldots m$ with high probability for $m = O(n)$, where $a_i \in \mathbb{R}^n$ are i.i.d standard Gaussian and $\eta \in \mathbb{R}^m$ has fixed sparse support and is otherwise arbitrary, by using a version of the PhaseMax algorithm augmented with slack variables subject to a penalty. This linear programming formulation, which we call RobustPhaseMax, operates in the natural parameter space, and our proofs rely on a direct analysis of the optimality conditions using concentration inequalities.

Connected through hyperedge HE-1 (induced by source paper "solving equations of random convex functions via anchored regression"):
  with members:
    - "learning relus via gradient descent"
    - "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "self calibration and biconvex compressive sensing"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "the generalized lasso with non linear observations"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-2 (induced by source paper "branchhull convex bilinear inversion from the entrywise product of signals with known signs"):
  with members:
    - "phasemax convex phase retrieval via basis pursuit"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "leveraging diversity and sparsity in blind deconvolution"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "a geometric analysis of phase retrieval"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "self calibration and biconvex compressive sensing"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-3 (induced by source paper "fundamental limits of phasemax for phase retrieval a replica analysis"):
  with members:
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "phasemax convex phase retrieval via basis pursuit"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "a field guide to forward backward splitting with a fasta implementation"
    - "sparse phase retrieval convex algorithms and limitations"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-4 (induced by source paper "phaseless sampling and reconstruction of real valued signals in shift invariant spaces"):
  with members:
    - "phase retrieval from multiple window short time fourier measurements"
    - "phase retrieval from the magnitudes of affine linear measurements"
    - "phase retrieval of real valued signals in a shift invariant space"
    - "spatially distributed sampling and reconstruction"
    - "phase retrieval an overview of recent developments"
    - "phaseless signal recovery in infinite dimensional spaces using structured modulations"
    - "phase retrieval from low rate samples"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-5 (induced by source paper "phasemax convex phase retrieval via basis pursuit"):
  with members:
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "coordinate descent algorithms for phase retrieval"
    - "phase retrieval via reweighted wirtinger flow"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "branchhull convex bilinear inversion from the entrywise product of signals with known signs"
    - "non convex phase retrieval from stft measurements"
    - "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space"

Connected through hyperedge HE-6 (induced by source paper "phase retrieval meets statistical learning theory a flexible convex relaxation"):
  with members:
    - "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "a geometric analysis of phase retrieval"
    - "phase retrieval an overview of recent developments"

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)