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: "spectral compressive sensing with polar interpolation"
  Abstract: Existing approaches to compressive sensing of frequency-sparse signals focuses on signal recovery rather than spectral estimation. Furthermore, the recovery performance is limited by the coherence of the required sparsity dictionaries and by the discretization of the frequency parameter space. In this paper, we introduce a greedy recovery algorithm that leverages a band-exclusion function and a polar interpolation function to address these two issues in spectral compressive sensing. Our algorithm is geared towards line spectral estimation from compressive measurements and outperforms most existing approaches in fidelity and tolerance to noise.

Connected through hyperedge HE-1 (induced by source paper "compressive parameter estimation via k median clustering"):
  with members:
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "robust spectral compressed sensing via structured matrix completion"
    - "matched filtering from limited frequency samples"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"

Connected through hyperedge HE-2 (induced by source paper "distributed compressed sensing off the grid"):
  with members:
    - "super resolution line spectrum estimation with block priors"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "direction of arrival estimation using co prime arrays a super resolution viewpoint"
    - "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"
    - "off the grid spectral compressed sensing with prior information"
    - "spectral compressed sensing via structured matrix completion"
    - "joint sparsity recovery for spectral compressed sensing"
    - "spectral compressive sensing with model selection"

Connected through hyperedge HE-3 (induced by source paper "compressive parameter estimation for sparse translation invariant signals using polar interpolation"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "compressive parameter estimation in awgn"
    - "compressive sensing for spread spectrum receivers"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "noise folding in compressed sensing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-4 (induced by source paper "greed is super a fast algorithm for super resolution"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "matched filtering from limited frequency samples"
    - "near minimax line spectral estimation"
    - "super resolution via superset selection and pruning"
    - "a tensor based method for missing traffic data completion"
    - "support detection in super resolution"
    - "super resolution from noisy data"

Connected through hyperedge HE-5 (induced by source paper "spectral compressive sensing with model selection"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "structured compressed sensing from theory to applications"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

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)