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 "block length dependent thresholds in block sparse compressed sensing"):
  Abstract: One of the most basic problems in compressed sensing is solving an under-determined system of linear equations. Although this problem seems rather hard certain l1-optimization algorithm appears to be very successful in solving it. The recent work of [14, 28] rigorously proved (in a large dimensional and statistical context) that if the number of equations (measurements in the compressed sensing terminology) in the system is proportional to the length of the unknown vector then there is a sparsity (number of non-zero elements of the unknown vector) also proportional to the length of the unknown vector such that l1-optimization algorithm succeeds in solving the system. In more recent papers [78,81] we considered the setup of the so-called block-sparse unknown vectors. In a large dimensional and statistical context, we determined sharp lower bounds on the values of allowable sparsity for any given number (proportional to the length of the unknown vector) of equations such that an l2/l1-optimization algorithm succeeds in solving the system. The results established in [78, 81] assumed a fairly large block-length of the block-sparse vectors. In this paper we consider the block-length to be a parameter of the system. Consequently, we then establish sharp lower bounds on the values of the allowable block-sparsity as functions of the block-length.

Includes members:
  Member "block sparse signals uncertainty relations and efficient recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dr2 net deep residual reconstruction network for image compressive sensing")
  Member "theoretical and empirical results for recovery from multiple measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the restricted isometry property of block diagonal matrices for group sparse signal recovery")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "weighted ell_1 minimization for sparse recovery with prior information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deterministic analysis of weighted bpdn with partially known support information")
  Member "average case analysis of multichannel sparse recovery using convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior")
  Member "various thresholds for ell_1 optimization in compressed sensing":
    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 recovery of positive signals with minimal expansion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perfect recovery conditions for non negative sparse modeling")
  Member "simultaneously sparse solutions to linear inverse problems with multiple system matrices and a single observation vector":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressed sensing of block sparse positive vectors")

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)