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: "simultaneously sparse solutions to linear inverse problems with multiple system matrices and a single observation vector"
  Abstract: A problem that arises in slice-selective magnetic resonance imaging (MRI) radio-frequency (RF) excitation pulse design is abstracted as a novel linear inverse problem with a simultaneous sparsity constraint. Multiple unknown signal vectors are to be determined, where each passes through a different system matrix and the results are added to yield a single observation vector. Given the matrices and lone observation, the objective is to find a simultaneously sparse set of unknown vectors that approximately solves the system. We refer to this as the multiple-system single-output (MSSO) simultaneous sparse approximation problem. This manuscript contrasts the MSSO problem with other simultaneous sparsity problems and conducts an initial exploration of algorithms with which to solve it. Greedy algorithms and techniques based on convex relaxation are derived and compared empirically. Experiments involve sparsity pattern recovery in noiseless and noisy settings and MRI RF pulse design.

Connected through hyperedge HE-1 (induced by source paper "compressed sensing of block sparse positive vectors"):
  with members:
    - "lifting l q optimization thresholds"
    - "optimality of ell_2 ell_1 optimization block length dependent thresholds"
    - "regularly random duality"
    - "lifting l 1 optimization strong and sectional thresholds"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "model based compressive sensing"
    - "weighted ell_1 minimization for sparse recovery with prior information"

Connected through hyperedge HE-2 (induced by source paper "optimality of ell_2 ell_1 optimization block length dependent thresholds"):
  with members:
    - "block sparse signals uncertainty relations and efficient recovery"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "block length dependent thresholds in block sparse compressed sensing"
    - "sparse recovery of positive signals with minimal expansion"

Connected through hyperedge HE-3 (induced by source paper "block length dependent thresholds in block sparse compressed sensing"):
  with members:
    - "block sparse signals uncertainty relations and efficient recovery"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "model based compressive sensing"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "sparse recovery of positive signals with minimal expansion"
    - "block sparsity coherence and efficient recovery"

Connected through hyperedge HE-4 (induced by source paper "various thresholds for ell_1 optimization in compressed sensing"):
  with members:
    - "block sparse signals uncertainty relations and efficient recovery"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "sparse recovery of positive signals with minimal expansion"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

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.NA(Numerical Analysis)