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: "compressed sensing matrices from fourier matrices"
  Abstract: The class of Fourier matrices is of special importance in compressed sensing (CS). This paper concerns deterministic construction of compressed sensing matrices from Fourier matrices. By using Katz' character sum estimation, we are able to design a deterministic procedure to select rows from a Fourier matrix to form a good compressed sensing matrix for sparse recovery. The sparsity bound in our construction is similar to that of binary CS matrices constructed by DeVore which greatly improves previous results for CS matrices from Fourier matrices. Our approach also provides more flexibilities in terms of the dimension of CS matrices. As a consequence, our construction yields an approximately mutually unbiased bases from Fourier matrices which is of particular interest to quantum information theory. This paper also contains a useful improvement to Katz' character sum estimation for quadratic extensions, with an elementary and transparent proof. Some numerical examples are included.

Connected through hyperedge HE-1 (induced by source paper "sensing matrix design and sparse recovery on the sphere and the rotation group"):
  with members:
    - "coherence optimization and best complex antipodal spherical codes"
    - "sparse image reconstruction on the sphere implications of a new sampling theorem"
    - "fast approximation of matrix coherence and statistical leverage"
    - "a novel sampling theorem on the sphere"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "signal recovery under cumulative coherence"):
  with members:
    - "sparse recovery with coherent tight frames via analysis dantzig selector and analysis lasso"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "compressed sensing and affine rank minimization under restricted isometry"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-3 (induced by source paper "a fast noniterative algorithm for compressive sensing using binary measurement matrices"):
  with members:
    - "living on the edge phase transitions in convex programs with random data"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "optimal phase transitions in compressed sensing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-4 (induced by source paper "hybrid mimo architectures for millimeter wave communications phase shifters or switches"):
  with members:
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "millimeter wave energy harvesting"
    - "wireless powered communication opportunities and challenges"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "what will 5g be"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "spatially sparse precoding in millimeter wave mimo systems"

Connected through hyperedge HE-5 (induced by source paper "a class of deterministic sensing matrices and their application in harmonic detection"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "exact joint sparse frequency recovery via optimization methods"
    - "compressed sensing off the grid"

Connected through hyperedge HE-6 (induced by source paper "discrete sampling and interpolation orthogonal interpolation for discrete bandlimited signals"):
  with members:
    - "discrete sampling and interpolation universal sampling sets for discrete bandlimited spaces"

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)