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: "the gap between the null space property and the restricted isometry property"
  Abstract: The null space property (NSP) and the restricted isometry property (RIP) are two properties which have received considerable attention in the compressed sensing literature. As the name suggests, NSP is a property that depends solely on the null space of the measurement procedure and as such, any two matrices which have the same null space will have NSP if either one of them does. On the other hand, RIP is a property of the measurement procedure itself, and given an RIP matrix it is straightforward to construct another matrix with the same null space that is not RIP. %Furthermore, RIP is known to imply NSP and therefore RIP is a strictly stronger assumption than NSP. We say a matrix is RIP-NSP if it has the same null space as an RIP matrix. We show that such matrices can provide robust recovery of compressible signals under Basis pursuit which in many applicable settings is comparable to the guarantee that RIP provides. More importantly, we constructively show that the RIP-NSP is stronger than NSP with the aid of this robust recovery result, which shows that RIP is fundamentally stronger than NSP.

Connected through hyperedge HE-1 (induced by source paper "preserving injectivity under subgaussian mappings and its application to compressed sensing"):
  with members:
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "robust width a characterization of uniformly stable and robust compressed sensing"
    - "a null space analysis of the l1 synthesis method in dictionary based compressed sensing"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "performance analysis of ell_1 synthesis with coherent frames"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-2 (induced by source paper "weak stability of ell_1 minimization methods in sparse data reconstruction"):
  with members:
    - "1 bit compressive sensing reformulation and rrsp based sign recovery theory"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "new bounds for restricted isometry constants"
    - "on polar polytopes and the recovery of sparse representations"

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)