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 "stability of modified cs and ls cs for recursive reconstruction of sparse signal sequences"):
  Abstract: In this work, we obtain sufficient conditions for the "stability" of our recently proposed algorithms, Least Squares Compressive Sensing residual (LS-CS) and modified-CS, for recursively reconstructing sparse signal sequences from noisy measurements. By "stability" we mean that the number of misses from the current support estimate and the number of extras in it remain bounded by a time-invariant value at all times. We show that, for a signal model with fixed signal power and support set size; support set changes allowed at every time; and gradual coefficient magnitude increase/decrease, "stability" holds under mild assumptions -- bounded noise, high enough minimum nonzero coefficient magnitude increase rate, and large enough number of measurements at every time. A direct corollary is that the reconstruction error is also bounded by a time-invariant value at all times. If the support set of the sparse signal sequence changes slowly over time, our results hold under weaker assumptions than what simple compressive sensing (CS) needs for the same error bound. Also, our support error bounds are small compared to the support size. Our discussion is backed up by Monte Carlo simulation based comparisons.

Includes members:
  Member "ls cs residual ls cs compressive sensing on least squares residual":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review")
  Member "dynamic updating for ell_ 1 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semi supervised sparse representation based classification for face recognition with insufficient labeled samples")
  Member "improved sparse recovery thresholds with two step reweighted ell_1 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time invariant error bounds for modified cs based sparse signal sequence recovery")
  Member "modified cs modifying compressive sensing for problems with partially known support":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multilinear compressive learning with prior knowledge")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive sensing for cut improvement and local clustering")

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)