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: "solving quadratic equations via phaselift when there are about as many equations as unknowns"
  Abstract: This note shows that we can recover a complex vector x in C^n exactly from on the order of n quadratic equations of the form | |^2 = b_i, i = 1, ..., m, by using a semidefinite program known as PhaseLift. This improves upon earlier bounds in [3], which required the number of equations to be at least on the order of n log n. We also demonstrate optimal recovery results from noisy quadratic measurements; these results are much sharper than previously known results.

Connected through hyperedge HE-1 (induced by source paper "phase retrieval of low rank matrices by anchored regression"):
  with members:
    - "blind phaseless short time fourier transform recovery"
    - "blind deconvolutional phase retrieval via convex programming"
    - "blind gain and phase calibration via sparse spectral methods"
    - "solving equations of random convex functions via anchored regression"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "low rank phase retrieval"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "self calibration via linear least squares"

Connected through hyperedge HE-2 (induced by source paper "complex phase retrieval from subgaussian measurements"):
  with members:
    - "on the convex geometry of blind deconvolution and matrix completion"
    - "phase retrieval without small ball probability assumptions"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "phasemax convex phase retrieval via basis pursuit"
    - "a geometric analysis of phase retrieval"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "stable low rank matrix recovery via null space properties"

Connected through hyperedge HE-3 (induced by source paper "optimal spectral initialization for signal recovery with applications to phase retrieval"):
  with members:
    - "optimization based amp for phase retrieval the impact of initialization and ell_2 regularization"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "sparse phase retrieval convex algorithms and limitations"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-4 (induced by source paper "on the global minimizers of real robust phase retrieval with sparse noise"):
  with members:
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-5 (induced by source paper "towards the optimal construction of a loss function without spurious local minima for solving quadratic equations"):
  with members:
    - "the non convex geometry of low rank matrix optimization"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "on the optimization landscape of tensor decompositions"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "phasemax convex phase retrieval via basis pursuit"
    - "matrix completion has no spurious local minimum"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "a geometric analysis of phase retrieval"

Connected through hyperedge HE-6 (induced by source paper "measurement matrix design for phase retrieval based on mutual information"):
  with members:
    - "non convex phase retrieval from stft measurements"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "intensity only optical compressive imaging using a multiply scattering material and a double phase retrieval approach"
    - "almost lossless analog compression without phase information"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "phase retrieval from coded diffraction patterns"
    - "phase retrieval stability and recovery guarantees"

Connected through hyperedge HE-7 (induced by source paper "learning relu networks via alternating minimization"):
  with members:
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "learning one hidden layer neural networks with landscape design"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "visualizing the loss landscape of neural nets"
    - "the multilinear structure of relu networks"

Connected through hyperedge HE-8 (induced by source paper "phaselin linear phase retrieval"):
  with members:
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "coordinate descent algorithms for phase retrieval"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "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"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "compressive phase retrieval via generalized approximate message passing"

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)