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: "near optimal phase retrieval of sparse vectors"
  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 several applications the signal in question is believed to be sparse. In this paper, we use ideas from the recently developed polarization method for phase retrieval and provide an algorithm that is guaranteed to recover a sparse signal from a number of phaseless linear measurements that scales linearly with the sparsity of the signal (up to logarithmic factors). This is particularly remarkable since it is known that a certain popular class of convex methods is not able to perform recovery unless the number of measurements scales with the square of the sparsity of the signal. This is a shorter version of a more complete publication that will appear elsewhere. 1

Connected through hyperedge HE-1 (induced by source paper "almost optimal phaseless compressed sensing with sublinear decoding time"):
  with members:
    - "phasemax convex phase retrieval via basis pursuit"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "fast and robust compressive phase retrieval with sparse graph codes"
    - "heavy hitters via cluster preserving clustering"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "super sparse signals with unknown phases efficiently recovered"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space"):
  with members:
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "sparse phase retrieval via truncated amplitude flow"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "phasemax convex phase retrieval via basis pursuit"
    - "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"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-3 (induced by source paper "phase retrieval an overview of recent developments"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "sparse phase retrieval from short time fourier measurements"
    - "phase retrieval with application to optical imaging"
    - "phase retrieval for sparse signals uniqueness conditions"
    - "phase retrieval from coded diffraction patterns"
    - "on conditions for uniqueness in sparse phase retrieval"
    - "sparse phase retrieval convex algorithms and limitations"

Connected through hyperedge HE-4 (induced by source paper "robustness properties of dimensionality reduction with gaussian random matrices"):
  with members:
    - "a strong restricted isometry property with an application to phaseless compressed sensing"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "random matrices and erasure robust frames"
    - "phase retrieval for sparse signals"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-5 (induced by source paper "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "reliable deniable and hidable communication over multipath networks"
    - "super sparse signals with unknown phases efficiently recovered"
    - "phase retrieval for sparse signals"
    - "new conditions for sparse phase retrieval"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "phase retrieval from low rate samples"

Connected through hyperedge HE-6 (induced by source paper "super sparse signals with unknown phases efficiently recovered"):
  with members:
    - "new conditions for sparse phase retrieval"
    - "gespar efficient phase retrieval of sparse signals"
    - "on conditions for uniqueness in sparse phase retrieval"
    - "sparse phase retrieval convex algorithms and limitations"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "sho fa robust compressive sensing with order optimal complexity measurements and bits"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"

Connected through hyperedge HE-7 (induced by source paper "a strong restricted isometry property with an application to phaseless compressed sensing"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "phase retrieval for sparse signals"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "on conditions for uniqueness in sparse phase retrieval"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "phase retrieval stability and recovery guarantees"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

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)