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: "ldpc codes for compressed sensing"
  Abstract: We present a mathematical connection between channel coding and compressed sensing. In particular, we link, on the one hand, channel coding linear programming decoding (CC-LPD), which is a well-known relaxation of maximum-likelihood channel decoding for binary linear codes, and, on the other hand, compressed sensing linear programming decoding (CS-LPD), also known as basis pursuit, which is a widely used linear programming relaxation for the problem of finding the sparsest solution of an underdetermined system of linear equations. More specifically, we establish a tight connection between CS-LPD based on a zero-one measurement matrix over the reals and CC-LPD of the binary linear channel code that is obtained by viewing this measurement matrix as a binary parity-check matrix. This connection allows the translation of performance guarantees from one setup to the other. The main message of this paper is that parity-check matrices of “good” channel codes can be used as provably “good” measurement matrices under basis pursuit. In particular, we provide the first deterministic construction of compressed sensing measurement matrices with an order-optimal number of rows using high-girth low-density parity-check codes constructed by Gallager.

Connected through hyperedge HE-1 (induced by source paper "unknown sparsity in compressed sensing denoising and inference"):
  with members:
    - "rounding sum of squares relaxations"
    - "correcting camera shake by incremental sparse approximation"
    - "estimating unknown sparsity in compressed sensing"
    - "one bit compressed sensing by linear programming"
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the stability of low rank matrix reconstruction a constrained singular value view"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical 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"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "vanishingly sparse matrices and expander graphs with application to compressed sensing"
    - "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 "deterministic construction of binary measurement matrices with flexible sizes"):
  with members:
    - "deterministic constructions of binary measurement matrices from finite geometry"
    - "deterministic construction of binary bipolar and ternary compressed sensing matrices"

Connected through hyperedge HE-4 (induced by source paper "near optimal binary compressed sensing matrix"):
  with members:
    - "sparse vector distributions and recovery from compressed sensing"
    - "sparse reconstruction via the reed muller sieve"
    - "compressed sensing how sharp is the restricted isometry property"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "deterministic construction of binary bipolar and ternary compressed sensing matrices"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"
    - "the statistical restricted isometry property and the wigner semicircle distribution of incoherent dictionaries"

Connected through hyperedge HE-5 (induced by source paper "deterministic constructions of binary measurement matrices from finite geometry"):
  with members:
    - "reconstruction guarantee analysis of binary measurement matrices based on girth"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "structured compressed sensing from theory to applications"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "deterministic construction of binary bipolar and ternary compressed sensing matrices"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-6 (induced by source paper "reconstruction guarantee analysis of binary measurement matrices based on girth"):
  with members:
    - "deterministic construction of binary bipolar and ternary compressed sensing matrices"
    - "pseudocodewords of tanner graphs"

Connected through hyperedge HE-7 (induced by source paper "verification based interval passing algorithm for compressed sensing"):
  with members:
    - "a simple message passing algorithm for compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "compressed sensing with incremental sparse measurements"):
  with members:
    - "analysis of verification based decoding on the q ary symmetric channel for large q"

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)