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: "optimal incorporation of sparsity information by weighted l 1 optimization"
  Abstract: Compressed sensing of sparse sources can be improved by incorporating prior knowledge of the source. In this paper we demonstrate a method for optimal selection of weights in weighted l 1  norm minimization for a noiseless reconstruction model, and show the improvements in compression that can be achieved.

Connected through hyperedge HE-1 (induced by source paper "rls recovery with asymmetric penalty fundamental limits and algorithmic approaches"):
  with members:
    - "precoding via approximate message passing with instantaneous signal constraints"
    - "maximum a posteriori signal recovery with prior information applications to compressive sensing"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "rsb decoupling property of map estimators"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "statistical mechanics of map estimation general replica ansatz"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "recovery of structured signals with prior information via maximizing correlation"):
  with members:
    - "stable recovery of structured signals from corrupted sub gaussian measurements"
    - "compressed sensing with prior information via maximizing correlation"
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "estimating unknown sparsity in compressed sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "analysis of sparse representations using bi orthogonal dictionaries"):
  with members:
    - "support recovery with sparsely sampled free random matrices"
    - "typical l 1 recovery limit of sparse vectors represented by concatenations of random orthogonal matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "analysis of mmse estimation for compressive sensing of block sparse signals"):
  with members:
    - "block sparse signals uncertainty relations and efficient recovery"
    - "model based compressive sensing"
    - "graphical models concepts in compressed sensing"
    - "statistical mechanical analysis of a typical reconstruction limit of compressed sensing"
    - "vector precoding for wireless mimo systems and its replica analysis"
    - "randomly spread cdma asymptotics via statistical physics"

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)