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 "on the performance bound of sparse estimation with sensing matrix perturbation"):
  Abstract: This paper focuses on the sparse estimation in the situation where both the the sensing matrix and the measurement vector are corrupted by additive Gaussian noises. The performance bound of sparse estimation is analyzed and discussed in depth. Two types of lower bounds, the constrained Cramer-Rao bound (CCRB) and the Hammersley-Chapman-Robbins bound (HCRB), are discussed. It is shown that the situation with sensing matrix perturbation is more complex than the one with only measurement noise. For the CCRB, its closed-form expression is deduced. It demonstrates a gap between the maximal and nonmaximal support cases. It is also revealed that a gap lies between the CCRB and the MSE of the oracle pseudoinverse estimator, but it approaches zero asymptotically when the problem dimensions tend to infinity. For a tighter bound, the HCRB, despite the difficulty in obtaining a simple expression for general sensing matrix, a closed-form expression in the unit sensing matrix case is derived for a qualitative study of the performance bound. It is shown that the gap between the maximal and nonmaximal cases is eliminated for the HCRB. Numerical simulations are performed to verify the theoretical results in this paper.

Includes members:
  Member "perturbation analysis of orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "preconditioned multiple orthogonal least squares and applications in ghost imaging via sparsity constraint")
  Member "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "proof of convergence and performance analysis for sparse recovery via zero point attracting projection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robustness of sparse recovery via f minimization a topological viewpoint")
  Member "sparsity cognizant total least squares for perturbed compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "noise folding in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "how well can we estimate a sparse vector":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation")
  Member "general deviants an analysis of perturbations in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "compressed sensing how sharp is the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")

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)