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 "new analysis of manifold embeddings and signal recovery from compressive measurements"):
  Abstract: Compressive Sensing (CS) exploits the surprising fact that the information contained in a sparse signal can be preserved in a small number of compressive, often random linear measurements of that signal. Strong theoretical guarantees have been established concerning the embedding of a sparse signal family under a random measurement operator and on the accuracy to which sparse signals can be recovered from noisy compressive measurements. In this paper, we address similar questions in the context of a different modeling framework. Instead of sparse models, we focus on the broad class of manifold models, which can arise in both parametric and non-parametric signal families. Using tools from the theory of empirical processes, we improve upon previous results concerning the embedding of low-dimensional manifolds under random measurement operators. We also establish both deterministic and probabilistic instance-optimal bounds in $\ell_2$ for manifold-based signal recovery and parameter estimation from noisy compressive measurements. In line with analogous results for sparsity-based CS, we conclude that much stronger bounds are possible in the probabilistic setting. Our work supports the growing evidence that manifold-based models can be used with high accuracy in compressive signal processing.

Includes members:
  Member "matched filtering from limited frequency samples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressive parameter estimation via k median clustering")
  Member "the restricted isometry property for random block diagonal matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sampling of graph signals via randomized local aggregations")
  Member "signal recovery on incoherent manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic aspects of inverse problems using generative models")
  Member "stable manifold embeddings with structured random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quickly finding the best linear model in high dimensions")
  Member "approximation of points on low dimensional manifolds via random linear projections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "new algorithms and improved guarantees for one bit compressed sensing on manifolds")
  Member "measurement bounds for sparse signal ensembles via graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "performance limits with additive error metrics in noisy multimeasurement vector problems")
  Member "beyond nyquist efficient sampling of sparse bandlimited signals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")
  Member "restricted isometries for partial random circulant matrices":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "random quadratic forms with dependence applications to restricted isometry and beyond")

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)