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 "tolerant compressed sensing with partially coherent sensing matrices"):
  Abstract: We consider compressed sensing (CS) using partially coherent sensing matrices. Most of CS theory to date is focused on incoherent sensing, that is, columns from the sensing matrix are highly uncorrelated. However, sensing systems with naturally occurring correlations arise in many applications, such as signal detection, motion detection and radar. Moreover, in these applications it is often not necessary to know the support of the signal exactly, but instead small errors in the support and signal are tolerable. In this paper, we focus on \hbox{$d$-tol}\-erant recovery, in which support set reconstructions are considered accurate when their locations match the true locations within $d$ indices. Despite the abundance of work utilizing incoherent sensing matrices, for \hbox{$d$-tol}\-erant recovery we suggest that coherence is actually \textit{beneficial}. This is especially true for situations with only a few and very noisy measurements as we demonstrate via numerical simulations. As a first step towards the theory of tolerant coherent sensing we introduce the notions of \hbox{$d$-co}\-herence and \hbox{$d$-tol}\-erant recovery. We then provide some theoretical arguments for a greedy algorithm applicable to \hbox{$d$-tol}\-erant recovery of signals with sufficiently spread support.

Includes members:
  Member "can we allow linear dependencies in the dictionary in the sparse synthesis framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spark level sparsity and the ell_1 tail minimization")
  Member "super resolution from noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "sub nyquist radar via doppler focusing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain")
  Member "analysis of block omp using block rip":
    also connected through other hyperedges:
      (none)
  Member "coherence pattern guided compressive sensing with unresolved grids":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "conditioning of restricted fourier matrices and super resolution of music")
  Member "deterministic construction of partial fourier compressed sensing matrices via cyclic difference sets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast correlation computation method for matching pursuit algorithms in compressed sensing")
  Member "block sparse signals uncertainty relations and efficient recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dr2 net deep residual reconstruction network for image 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)