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: "a simple message passing algorithm for compressed sensing"
  Abstract: We consider the recovery of a nonnegative vector x from measurements y = Ax, where A is an m-by-n matrix whos entries are in {0, 1}. We establish that when A corresponds to the adjacency matrix of a bipartite graph with sufficient expansion, a simple message-passing algorithm produces an estimate \hat{x} of x satisfying ||x-\hat{x}||_1 \leq O(n/k) ||x-x(k)||_1, where x(k) is the best k-sparse approximation of x. The algorithm performs O(n (log(n/k))^2 log(k)) computation in total, and the number of measurements required is m = O(k log(n/k)). In the special case when x is k-sparse, the algorithm recovers x exactly in time O(n log(n/k) log(k)). Ultimately, this work is a further step in the direction of more formally developing the broader role of message-passing algorithms in solving compressed sensing problems.

Connected through hyperedge HE-1 (induced by source paper "algebraic optimization of binary spatially coupled measurement matrices for interval passing"):
  with members:
    - "failure analysis of the interval passing algorithm for compressed sensing"
    - "a generalized algebraic approach to optimizing sc ldpc codes"
    - "on failing sets of the interval passing algorithm for compressed sensing"
    - "on the minimum distance of array based spatially coupled low density parity check codes"
    - "verification based interval passing algorithm for compressed sensing"
    - "the effect of spatial coupling on compressive sensing"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-2 (induced by source paper "failure analysis of the interval passing algorithm for compressed sensing"):
  with members:
    - "asymptotic analysis and spatial coupling of counter braids"
    - "on the minimum stopping distance of array low density parity check codes"
    - "verification based interval passing algorithm for compressed sensing"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "message passing algorithms for compressed sensing"
    - "on the stopping distance and the stopping redundancy of codes"

Connected through hyperedge HE-3 (induced by source paper "asymptotic analysis and spatial coupling of counter braids"):
  with members:
    - "spatially coupled turbo like codes"
    - "deterministic and ensemble based spatially coupled product codes"
    - "a simple proof of maxwell saturation for coupled scalar recursions"
    - "spatially coupled ldpc codes for decode and forward relaying of two correlated sources over the bec"
    - "approaching the rate distortion limit with spatial coupling belief propagation and decimation"
    - "threshold saturation in spatially coupled constraint satisfaction problems"
    - "lossy source coding via spatially coupled ldgm ensembles"
    - "approaching capacity at high rates with iterative hard decision decoding"

Connected through hyperedge HE-4 (induced by source paper "on failing sets of the interval passing algorithm for compressed sensing"):
  with members:
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"

Connected through hyperedge HE-5 (induced by source paper "verification based interval passing algorithm for compressed sensing"):
  with members:
    - "ldpc codes for compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "density evolution analysis of node based verification based algorithms in compressive sensing"):
  with members:
    - "on the iterative decoding of high rate ldpc codes with applications in compressed sensing"
    - "bayesian compressive sensing via belief propagation"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-7 (induced by source paper "graphical models concepts in compressed sensing"):
  with members:
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "recovering low rank matrices from few coefficients in any basis"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "a probabilistic and ripless theory of compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "model based compressive sensing"
    - "imp a message passing algorithmfor matrix completion"

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)