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 "preserving injectivity under subgaussian mappings and its application to compressed sensing"):
  Abstract: The field of compressed sensing has become a major tool in high-dimensional analysis, with the realization that vectors can be recovered from relatively very few linear measurements as long as the vectors lie in a low-dimensional structure, typically the vectors that are zero in most coordinates with respect to a basis. However, there are many applications where we instead want to recover vectors that are sparse with respect to a dictionary rather than a basis. That is, we assume the vectors are linear combinations of at most $s$ columns of a $d \times n$ matrix $\mathbf{D}$, where $s$ is very small relative to $n$ and the columns of $\mathbf{D}$ form a (typically overcomplete) spanning set. #R##N#In this direction, we show that as a matrix $\mathbf{D}$ stays bounded away from zero in norm on a set $S$ and a provided map ${\boldsymbol \Phi}$ comprised of i.i.d. subgaussian rows has number of measurements at least proportional to the square of $w(\mathbf{D}S)$, the Gaussian width of the related set $\mathbf{D}S$, then with high probability the composition ${\boldsymbol \Phi} \mathbf{D}$ also stays bounded away from zero. This result has potential as a powerful tool in dimension reduction analysis. As a specific application, we obtain that the null space property of order $s$ is preserved under such subgaussian maps with high probability, which is nearly optimal in the sense that we only need $O(s\log(n))$ random measurements and a minimal condition on $\mathbf{D}$. Consequently we obtain stable recovery guarantees for dictionary-sparse signals via the $\ell_1$-synthesis method, a typically challenging task, even with random measurements.

Includes members:
  Member "improved bounds for sparse recovery from subsampled random convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "random quadratic forms with dependence applications to restricted isometry and beyond")
  Member "compressive sensing with redundant dictionaries and structured measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications")
  Member "the gap between the null space property and the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weak stability of ell_1 minimization methods in sparse data reconstruction")
  Member "robust width a characterization of uniformly stable and robust compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse recovery based on q ratio constrained minimal singular values")
  Member "a null space analysis of the l1 synthesis method in dictionary based compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spark level sparsity and the ell_1 tail minimization")
  Member "signal space cosamp for sparse recovery with redundant dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "data driven algorithm selection and parameter tuning two case studies in optimization and signal processing")
  Member "performance analysis of ell_1 synthesis with coherent frames":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a null space analysis of the l1 synthesis method in dictionary based compressed sensing")
  Member "sparse recovery with orthogonal matching pursuit under rip":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

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)