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: "frequency estimation of multiple sinusoids with three sub nyquist channels"
  Abstract: Frequency estimation of multiple sinusoids is significant in both theory and application. In some application scenarios, only sub-Nyquist samples are available to estimate the frequencies. A conventional approach is to sample the signals at several lower rates. In this paper, we propose a novel method based on subspace techniques using three-channel undersampled data. We analyze the impact of undersampling and demonstrate that three sub-Nyquist channels are general enough to estimate the frequencies provided the undersampling ratios are coprime. The ambiguous frequencies obtained from one channel are identified and the correct frequencies are screened out by using three-channel samples jointly. Numerical experiments verify the correctness of our analysis and conclusion. Simulations show that the proposed method is valid and with high accuracy.

Connected through hyperedge HE-1 (induced by source paper "line spectral estimation based on compressed sensing with deterministic sub nyquist sampling"):
  with members:
    - "a class of deterministic sensing matrices and their application in harmonic detection"
    - "robust spectral compressed sensing via structured matrix completion"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"
    - "variational relevance vector machines"
    - "robustly stable signal recovery in compressed sensing with structured matrix perturbation"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-2 (induced by source paper "an algorithm of frequency estimation for multi channel coprime sampling"):
  with members:
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"

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)