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: "typical l 1 recovery limit of sparse vectors represented by concatenations of random orthogonal matrices"
  Abstract: We consider the problem of recovering an N-dimensional sparse vector x from its linear transformation y = Dx of M (<N) dimensions. Minimization of the l(1)-norm of x under the constraint y = Dx  ...

Connected through hyperedge HE-1 (induced by source paper "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"):
  with members:
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "s amp approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "on sparse vector recovery performance in structurally orthogonal matrices via lasso"
    - "asymptotic mmse analysis under sparse representation modeling"
    - "statistical mechanics approach to sparse noise denoising"

Connected through hyperedge HE-2 (induced by source paper "on sparse vector recovery performance in structurally orthogonal matrices via lasso"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"
    - "multi processor approximate message passing using lossy compression"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"

Connected through hyperedge HE-3 (induced by source paper "design and analysis of a greedy pursuit for distributed compressed sensing"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "greedy sparsity promoting algorithms for distributed learning"
    - "analysis of democratic voting principles used in distributed greedy algorithms"
    - "distributed compressed sensing for static and time varying networks"
    - "distributed greedy pursuit algorithms"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "projection based and look ahead strategies for atom selection"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "analysis of compressed sensing with spatially coupled orthogonal matrices"):
  with members:
    - "support recovery with sparsely sampled free random matrices"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "the effect of spatial coupling on compressive sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "statistical mechanical analysis of the kronecker channel model for multiple input multiple output wireless communication"
    - "message passing algorithms for compressed sensing"
    - "analysis of cdma systems that are characterized by eigenvalue spectrum"

Connected through hyperedge HE-5 (induced by source paper "statistical mechanics approach to sparse noise denoising"):
  with members:
    - "large system analysis of joint channel and data estimation for mimo ds cdma systems"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "randomly spread cdma asymptotics via statistical physics"

Connected through hyperedge HE-6 (induced by source paper "analysis of sparse representations using bi orthogonal dictionaries"):
  with members:
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "optimal incorporation of sparsity information by weighted l 1 optimization"
    - "statistical mechanical analysis of compressed sensing utilizing correlated compression matrix"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "message passing algorithms for compressed sensing"

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)