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 "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.

Includes members:
  Member "the restricted isometry property of subsampled fourier matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "one shot neural architecture search via compressive sensing")
  Member "on the gap between rip properties and sparse recovery conditions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver")
  Member "robust width a characterization of uniformly stable and robust compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse recovery based on q ratio constrained minimal singular values")
  Member "sparse representation of a polytope and recovery of sparse signals and low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the importance of phase in complex compressive sensing")
  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 "preserving injectivity under subgaussian mappings and its application to compressed sensing")
  Member "stable and robust sampling strategies for compressive imaging":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "sparse approximation property and stable recovery of sparse signals from noisy measurements":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "matrix recovery from rank one projection measurements via nonconvex minimization")
  Member "phase transitions for greedy sparse approximation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")

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)