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 with polarization"
  Abstract: In many areas of imaging science, it is difficult to measure the phase of linear measurements. As such, one often wishes to reconstruct a signal from intensity measurements, that is, perform phase retrieval. In this paper, we provide a novel measurement design which is inspired by interferometry and exploits certain properties of expander graphs. We also give an efficient phase retrieval procedure, and use recent results in spectral graph theory to produce a stable performance guarantee which rivals the guarantee for PhaseLift in [Candes et al. 2011]. We use numerical simulations to illustrate the performance of our phase retrieval procedure, and we compare reconstruction error and runtime with a common alternating-projections-type procedure.

Connected through hyperedge HE-1 (induced by source paper "hardness results for structured linear systems"):
  with members:
    - "computing maximum flow with augmenting electrical flows"
    - "geometric median in nearly linear time"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "efficient inverse maintenance and faster algorithms for linear programming"
    - "sketching as a tool for numerical linear algebra"
    - "approximate undirected maximum flows in o m polylog n time"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "stable camera motion estimation using convex programming"

Connected through hyperedge HE-2 (induced by source paper "improved recovery guarantees for phase retrieval from coded diffraction patterns"):
  with members:
    - "a partial derandomization of phaselift using spherical designs"
    - "low rank matrix recovery from rank one measurements"
    - "ripless compressed sensing from anisotropic measurements"
    - "phase retrieval from low rate samples"
    - "phase retrieval from coded diffraction patterns"
    - "phase retrieval stability and recovery guarantees"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-3 (induced by source paper "a geometric analysis of phase retrieval"):
  with members:
    - "non convex phase retrieval from stft measurements"
    - "phasemax convex phase retrieval via basis pursuit"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "phase retrieval via incremental truncated wirtinger flow"
    - "phaseless rcovery using gauss newton method"

Connected through hyperedge HE-4 (induced by source paper "improving compressed sensing with the diamond norm"):
  with members:
    - "comments on improving compressed sensing with the diamond norm saturation of the norm inequalities between diamond and nuclear norm"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "tensor theta norms and low rank recovery"
    - "self calibration and biconvex compressive sensing"
    - "stable low rank matrix recovery via null space properties"
    - "a partial derandomization of phaselift using spherical designs"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"

Connected through hyperedge HE-5 (induced by source paper "explicit frames for deterministic phase retrieval via phaselift"):
  with members:
    - "a partial derandomization of phaselift using spherical designs"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "fast and robust compressive phase retrieval with sparse graph codes"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "sub linear time support recovery for compressed sensing using sparse graph codes"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "sparse phase retrieval convex algorithms and limitations"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-7 (induced by source paper "fast phase retrieval for high dimensions a block based approach"):
  with members:
    - "intensity only optical compressive imaging using a multiply scattering material and a double phase retrieval approach"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "compressive phase retrieval via generalized approximate message passing"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "gespar efficient phase retrieval of sparse signals"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-8 (induced by source paper "solving random quadratic systems of equations is nearly as easy as solving linear systems"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "guaranteed matrix completion via non convex factorization"
    - "simple efficient and neural algorithms for sparse coding"
    - "a partial derandomization of phaselift using spherical designs"
    - "compressive phase retrieval via generalized approximate message passing"
    - "complete dictionary recovery over the sphere"
    - "efficient compressive phase retrieval with constrained sensing vectors"
    - "phase retrieval via wirtinger flow theory 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)