human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "spark level sparsity and the ell_1 tail minimization"):
  Abstract: Solving compressed sensing problems relies on the properties of sparse signals. It is commonly assumed that the sparsity s needs to be less than one half of the spark of the sensing matrix A, and then the unique sparsest solution exists, and recoverable by $\ell_1$-minimization or related procedures. We discover, however, a measure theoretical uniqueness exists for nearly spark-level sparsity from compressed measurements Ax = b. Specifically, suppose A is of full spark with m rows, and suppose $\frac{m}{2}$   $\frac{m}{2}$ in thousands and thousands of random tests. We further show instead that the mere $\ell_1$-minimization would actually fail if s > $\frac{m}{2}$ even from the same measure theoretical point of view.

Includes members:
  Member "a null space analysis of the l1 synthesis method in dictionary based compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "preserving injectivity under subgaussian mappings and its application to compressed sensing")
  Member "can we allow linear dependencies in the dictionary in the sparse synthesis framework":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tolerant compressed sensing with partially coherent sensing matrices")
  Member "signal space cosamp for sparse recovery with redundant dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "data driven algorithm selection and parameter tuning two case studies in optimization and signal processing")
  Member "compressed sensing with general frames via optimal dual based ell _ 1 analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in pde")
  Member "sparse signal reconstruction via iterative support detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "truncated sparse approximation property and truncated q norm minimization")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressive sensing for cut improvement and local clustering")

Question: Please tell me which class the source paper 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)