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: "theory and applications of compressed sensing"
  Abstract: Compressed sensing is a novel research area, which was introduced in 2006, and since then has already become a key concept in various areas of applied mathematics, computer science, and electrical engineering. It surprisingly predicts that high-dimensional signals, which allow a sparse representation by a suitable basis or, more generally, a frame, can be recovered from what was previously considered highly incomplete linear measurements by using efficient algorithms. This article shall serve as an introduction to and a survey about compressed sensing.

Connected through hyperedge HE-1 (induced by source paper "exploiting prior knowledge in compressed sensing wireless ecg systems"):
  with members:
    - "variational bayesian algorithm for quantized compressed sensing"
    - "cramer rao type bounds for sparse bayesian learning"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "structured compressed sensing from theory to applications"
    - "sparse signal reconstruction via iterative support detection"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "separation of sinusoidal and chirp components using compressive sensing approach"):
  with members:
    - "applications of compressed sensing in communications networks"

Connected through hyperedge HE-3 (induced by source paper "omp based joint sparsity pattern recovery under communication constraints"):
  with members:
    - "distributed greedy pursuit algorithms"
    - "subspace recovery from structured union of subspaces"
    - "applications of compressed sensing in communications networks"
    - "distributed sparse signal recovery for sensor networks"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "performance analysis for sparse support recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "compressive imaging and characterization of sparse light deflection maps"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "compressive optical deflectometric tomography a constrained total variation minimization approach"
    - "spread spectrum magnetic resonance imaging"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "compressive schlieren deflectometry"
    - "compressed sensing performance bounds under poisson noise"

Connected through hyperedge HE-5 (induced by source paper "off the grid spectral compressed sensing with prior information"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "weighted ell_1 minimization for sparse recovery with prior information"

Connected through hyperedge HE-6 (induced by source paper "precise semidefinite programming formulation of atomic norm minimization for recovering d dimensional d geq 2 off the grid frequencies"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-7 (induced by source paper "sparse recovery with graph constraints"):
  with members:
    - "link delay estimation via expander graphs"
    - "sparse recovery with graph constraints fundamental limits and measurement construction"
    - "compressive sensing over graphs"
    - "graph constrained group testing"
    - "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)