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 restricted isometry property of block diagonal matrices for group sparse signal recovery"):
  Abstract: Group-sparsity is a common low-complexity signal model with widespread application across various domains of science and engineering. The recovery of such signal ensembles from compressive measurements has been extensively studied in the literature under the assumption that measurement operators are modeled as densely populated random matrices. In this paper, we turn our attention to an acquisition model intended to ease the energy consumption of sensing devices by splitting the measurements up into distinct signal blocks. More precisely, we present uniform guarantees for group-sparse signal recovery in the scenario where a number of sensors obtain independent partial signal observations modeled by block diagonal measurement matrices. We establish a group-sparse variant of the classical restricted isometry property for block diagonal sensing matrices acting on group-sparse vectors, and provide conditions under which subgaussian block diagonal random matrices satisfy this group-RIP with high probability. Two different scenarios are considered in particular. In the first scenario, we assume that each sensor is equipped with an independently drawn measurement matrix. We later lift this requirement by considering measurement matrices with constant block diagonal entries. In other words, every sensor is equipped with a copy of the same prototype matrix. The problem of establishing the group-RIP is cast into a form in which one needs to establish the concentration behavior of the suprema of chaos processes which involves estimating Talagrand's $\gamma_2$ functional. As a side effect of the proof, we present an extension to Maurey's empirical method to provide new bounds on the covering number of sets consisting of finite convex combinations of possibly infinite sets.

Includes members:
  Member "gelfand numbers related to structured sparsity and besov space embeddings with small mixed smoothness":
    also connected through other hyperedges:
      (none)
  Member "uniform recovery from subgaussian multi sensor measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparsity undersampling tradeoffs in anisotropic undersampling with applications in mr imaging spectroscopy")
  Member "uniform recovery of fusion frame structured sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "the restricted isometry property for random block diagonal matrices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sampling of graph signals via randomized local aggregations")
  Member "theoretical and empirical results for recovery from multiple measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse methods for direction of arrival estimation")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressed sensing with invertible generative models and dependent 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)