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 "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"):
  Abstract: In compressed sensing, it is often desirable to consider signals possessing additional structure beyond sparsity. One such structured signal model - which forms the focus of this paper - is the local sparsity in levels class. This class has recently found applications in problems such as compressive imaging, multi-sensor acquisition systems and sparse regularization in inverse problems. In this paper we present uniform recovery guarantees for this class when the measurement matrix corresponds to a subsampled isometry. We do this by establishing a variant of the standard restricted isometry property for sparse in levels vectors, known as the restricted isometry property in levels. Interestingly, besides the usual log factors, our uniform recovery guarantees are simpler and less stringent than existing nonuniform recovery guarantees. For the particular case of discrete Fourier sampling with Haar wavelet sparsity, a corollary of our main theorem yields a new recovery guarantee which improves over the current state-of-the-art.

Includes members:
  Member "uniform recovery from subgaussian multi sensor measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the restricted isometry property of block diagonal matrices for group sparse signal recovery")
  Member "compressed sensing and parallel acquisition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on oracle type local recovery guarantees in compressed sensing")
  Member "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressed sensing and parallel acquisition")
  Member "optimal sparse recovery for multi sensor measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressed sensing and parallel acquisition")
  Member "compressed sensing with structured sparsity and structured acquisition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis")
  Member "refined analysis of sparse mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")
  Member "structure dependent sampling in compressed sensing theoretical guarantees for tight frames":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "do log factors matter on optimal wavelet approximation and the foundations of compressed sensing")
  Member "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the importance of phase in complex compressive sensing")

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)