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 "time invariant error bounds for modified cs based sparse signal sequence recovery"):
  Abstract: In this work, we obtain performance guarantees for modified-CS and for its improved version, modified-CS-Add-LS-Del, for recursive reconstruction of a time sequence of sparse signals from a reduced set of noisy measurements available at each time. Under mild assumptions, we show that the support recovery error of both algorithms is bounded by a time-invariant and small value at all times. The same is also true for the reconstruction error. Under a slow support change assumption, (i) the support recovery error bound is small compared to the support size; and (ii) our results hold under weaker assumptions on the number of measurements than what $\ell_1$ minimization for noisy data needs. We first give a general result that only assumes a bound on support size, number of support changes and number of small magnitude nonzero entries at each time. Later, we specialize the main idea of these results for two sets of signal change assumptions that model the class of problems in which a new element that is added to the support either gets added at a large initial magnitude or its magnitude slowly increases to a large enough value within a finite delay. Simulation experiments are shown to back up our claims.

Includes members:
  Member "on partial sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the exact recovery conditions of 3d human motion from 2d landmark motion with sparse articulated motion")
  Member "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "minimax optimal online stochastic learning for sequences of convex functions under sub gradient observation failures")
  Member "subspace methods for joint sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "exact dynamic support tracking with multiple measurement vectors using compressive music":
    also connected through other hyperedges:
      (none)
  Member "support predicted modified cs for recursive robust principal components pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provable low rank phase retrieval and compressive pca")
  Member "ls cs residual ls cs compressive sensing on least squares residual":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review")
  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")
  Member "sparse signal reconstruction via iterative support detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "truncated sparse approximation property and truncated q norm minimization")

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)