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: "on the iterative decoding of high rate ldpc codes with applications in compressed sensing"
  Abstract: This paper considers the performance of $(j,k)$-regular low-density parity-check (LDPC) codes with message-passing (MP) decoding algorithms in the high-rate regime. In particular, we derive the high-rate scaling law for MP decoding of LDPC codes on the binary erasure channel (BEC) and the $q$-ary symmetric channel ($q$-SC). For the BEC, the density evolution (DE) threshold of iterative decoding scales like $\Theta(k^{-1})$ and the critical stopping ratio scales like $\Theta(k^{-j/(j-2)})$. For the $q$-SC, the DE threshold of verification decoding depends on the details of the decoder and scales like $\Theta(k^{-1})$ for one decoder. #R##N#Using the fact that coding over large finite alphabets is very similar to coding over the real numbers, the analysis of verification decoding is also extended to the the compressed sensing (CS) of strictly-sparse signals. A DE based approach is used to analyze the CS systems with randomized-reconstruction guarantees. This leads to the result that strictly-sparse signals can be reconstructed efficiently with high-probability using a constant oversampling ratio (i.e., when the number of measurements scales linearly with the sparsity of the signal). A stopping-set based approach is also used to get stronger (e.g., uniform-in-probability) reconstruction guarantees.

Connected through hyperedge HE-1 (induced by source paper "ldpc codes for compressed sensing"):
  with members:
    - "counting in graph covers a combinatorial characterization of the bethe entropy function"
    - "compressed sensing how sharp is the restricted isometry property"
    - "compressed sensing performance bounds under poisson noise"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "lp decoding meets lp decoding a connection between channel coding and compressed sensing"
    - "absdet pseudo codewords and perm pseudo codewords definitions and properties"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "probabilistic analysis of linear programming decoding"

Connected through hyperedge HE-2 (induced by source paper "analysis and design of irregular graphs for node based verification based recovery algorithms in compressed sensing"):
  with members:
    - "compressive identification of linear operators"
    - "collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks"
    - "analysis of verification based decoding on the q ary symmetric channel for large q"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-3 (induced by source paper "density evolution analysis of node based verification based algorithms in compressive sensing"):
  with members:
    - "a simple message passing algorithm for compressed sensing"
    - "bayesian compressive sensing via belief propagation"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-4 (induced by source paper "the effect of spatial coupling on compressive sensing"):
  with members:
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "coupled graphical models and their thresholds"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "threshold saturation on bms channels via spatial coupling"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "asymptotically good ldpc convolutional codes based on protographs"

Connected through hyperedge HE-5 (induced by source paper "an efficient approach toward the asymptotic analysis of node based recovery algorithms in compressed sensing"):
  with members:
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing ii analysis and validation"
    - "compressive sensing using low density frames"
    - "analysis of verification based decoding on the q ary symmetric channel for large q"

Connected through hyperedge HE-6 (induced by source paper "lp decoding meets lp decoding a connection between channel coding and compressed sensing"):
  with members:
    - "absdet pseudo codewords and perm pseudo codewords definitions and properties"
    - "probabilistic analysis of linear programming decoding"
    - "a decomposition theory for binary linear codes"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"
    - "the benefit of thresholding in lp decoding of ldpc codes"
    - "pseudocodewords of tanner graphs"

Connected through hyperedge HE-7 (induced by source paper "message passing algorithms for compressed sensing"):
  with members:
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "efficient and robust compressed sensing using high quality expander graphs"
    - "max product for maximum weight matching convergence correctness and lp duality"
    - "bayesian compressive sensing via belief propagation"

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)