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: "vanishingly sparse matrices and expander graphs with application to compressed sensing"
  Abstract: We revisit the probabilistic construction of sparse random matrices where each column has a fixed number of nonzeros whose row indices are drawn uniformly at random with replacement. These matrices have a one-to-one correspondence with the adjacency matrices of fixed left degree expander graphs. We present formulae for the expected cardinality of the set of neighbors for these graphs, and present tail bounds on the probability that this cardinality will be less than the expected value. Deducible from these bounds are similar bounds for the expansion of the graph which is of interest in many applications. These bounds are derived through a more detailed analysis of collisions in unions of sets. Key to this analysis is a novel {\em dyadic splitting} technique. The analysis led to the derivation of better order constants that allow for quantitative theorems on existence of lossless expander graphs and hence the sparse random matrices we consider and also quantitative compressed sensing sampling theorems when using sparse non mean-zero measurement matrices.

Connected through hyperedge HE-1 (induced by source paper "on the construction of sparse matrices from expander graphs"):
  with members:
    - "on model based rip 1 matrices"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "compressed sensing how sharp is the restricted isometry property"

Connected through hyperedge HE-2 (induced by source paper "sparsification of matrices and compressed sensing"):
  with members:
    - "deterministic constructions of binary measurement matrices from finite geometry"
    - "near optimal binary compressed sensing matrix"
    - "ldpc codes for compressed sensing"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "restricted isometries for partial random circulant matrices"
    - "performance bounds for expander based compressed sensing in the presence of poisson noise"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-3 (induced by source paper "structured random measurements in signal processing"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "a partial derandomization of phaselift using spherical designs"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "convolutional compressed sensing using deterministic sequences"
    - "gespar efficient phase retrieval of sparse signals"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "phase retrieval from coded diffraction patterns"
    - "compressive multiplexing of correlated signals"

Connected through hyperedge HE-4 (induced by source paper "on construction and analysis of sparse random matrices and expander graphs with applications to compressed sensing"):
  with members:
    - "phase transitions for greedy sparse approximation algorithms"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

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)