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: "lp decoding meets lp decoding a connection between channel coding and compressed sensing"
  Abstract: This is a tale of two linear programming decoders, namely channel coding linear programming decoding (CC-LPD) and compressed sensing linear programming decoding (CS-LPD). So far, they have evolved quite independently. The aim of the present paper is to show that there is a tight connection between, on the one hand, CS-LPD based on a zero-one measurement matrix over the reals and, on the other hand, CC-LPD of the binary linear code that is obtained by viewing this measurement matrix as a binary parity-check matrix. This connection allows one to translate performance guarantees from one setup to the other.

Connected through hyperedge HE-1 (induced by source paper "bi level protected compressive sampling"):
  with members:
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "low complexity multiclass encryption by compressed sensing"
    - "low complexity cloud image privacy protection via matrix perturbation"
    - "joint quantization and diffusion for compressed sensing measurements of natural images"
    - "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"
    - "ripless compressed sensing from anisotropic measurements"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "towards secure compressive sampling scheme"):
  with members:
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "low complexity multiclass encryption by compressed sensing"
    - "low complexity cloud image privacy protection via matrix perturbation"
    - "joint quantization and diffusion for compressed sensing measurements of natural images"
    - "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"
    - "embedding cryptographic features in compressive sensing"
    - "ripless compressed sensing from anisotropic measurements"
    - "permutation enhanced parallel reconstruction with a linear compressive sampling device"

Connected through hyperedge HE-3 (induced by source paper "on finite alphabet compressive sensing"):
  with members:
    - "approximate message passing under finite alphabet constraints"
    - "coding theoretic methods for sparse recovery"

Connected through hyperedge HE-4 (induced by source paper "rank minimization over finite fields fundamental limits and coding theoretic interpretations"):
  with members:
    - "information theoretic bounds for tensor rank minimization over finite fields"
    - "capacity achieving linear codes with random binary sparse generating matrices"
    - "capacity achieving random sparse linear codes"
    - "information theoretic bounds for low rank matrix completion"
    - "distributed storage codes meet multiple access wiretap channels"
    - "communication over finite field matrix channels"
    - "the power of convex relaxation near optimal matrix completion"
    - "guaranteed rank minimization via singular value projection"

Connected through hyperedge HE-5 (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"
    - "absdet pseudo codewords and perm pseudo codewords definitions and properties"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "on the iterative decoding of high rate ldpc codes with applications in compressed sensing"
    - "probabilistic analysis of linear programming decoding"

Connected through hyperedge HE-6 (induced by source paper "structured sublinear compressive sensing via belief propagation"):
  with members:
    - "sparse reconstruction via the reed muller sieve"
    - "multiple bases belief propagation decoding of high density cyclic codes"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "message passing algorithms for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "bayesian compressive sensing via belief propagation"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"
    - "encoding for the blackwell channel with reinforced belief propagation"

Connected through hyperedge HE-7 (induced by source paper "worst configurations instantons for compressed sensing over reals a channel coding approach"):
  with members:
    - "sequential compressed sensing"
    - "the power of convex relaxation near optimal matrix completion"
    - "efficient implementation of linear programming decoding"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "provably efficient instanton search algorithm for lp decoding of ldpc codes over the bsc"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "reweighted lp decoding for ldpc codes"):
  with members:
    - "probabilistic analysis of linear programming decoding"
    - "loop calculus helps to improve belief propagation and linear programming decodings of low density parity check codes"
    - "towards low complexity linear programming decoding"

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)