human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "deterministic performance analysis of subspace methods for cisoid parameter estimation"
  Abstract: Performance analyses of subspace algorithms for cisoid parameter estimation available in the literature are predominantly of statistical nature with a focus on asymptotic$-$either in the sample size or the SNR$-$statements. This paper presents a deterministic, finite sample size, and finite-SNR performance analysis of the ESPRIT algorithm and the matrix pencil method. Our results are based, inter alia, on a new upper bound on the condition number of Vandermonde matrices with nodes inside the unit disk. This bound is obtained through a generalization of Hilbert's inequality frequently used in large sieve theory.

Connected through hyperedge HE-1 (induced by source paper "super resolution limit of the esprit algorithm"):
  with members:
    - "stable super resolution limit and smallest singular value of restricted fourier matrices"
    - "elementary l error estimates for super resolution de noising"
    - "compressive spectral estimation with single snapshot esprit stability and resolution"
    - "super resolution of positive sources the discrete setup"
    - "the recoverability limit for superresolution via sparsity"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "compressed sensing off the grid"

Connected through hyperedge HE-2 (induced by source paper "compressive sampling using annihilating filter based low rank interpolation"):
  with members:
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "sparse low rank decomposition of annihilating filter based hankel matrix for impulse noise removal"
    - "incoherence optimal matrix completion"
    - "robust spectral compressed sensing via structured matrix completion"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "near minimax line spectral estimation"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"

Question: Please tell me which class the center node 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)