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: "identification of parametric underspread linear systems and super resolution radar"
  Abstract: Identification of time-varying linear systems, which introduce both time-shifts (delays) and frequency-shifts (Doppler-shifts), is a central task in many engineering applications. This paper studies the problem of identification of underspread linear systems (ULSs), whose responses lie within a unit-area region in the delay-Doppler space, by probing them with a known input signal. It is shown that sufficiently-underspread parametric linear systems, described by a finite set of delays and Doppler-shifts, are identifiable from a single observation as long as the time-bandwidth product of the input signal is proportional to the square of the total number of delay-Doppler pairs in the system. In addition, an algorithm is developed that enables identification of parametric ULSs from an input train of pulses in polynomial time by exploiting recent results on sub-Nyquist sampling for time delay estimation and classical results on recovery of frequencies from a sum of complex exponentials. Finally, application of these results to super-resolution target detection using radar is discussed. Specifically, it is shown that the proposed procedure allows to distinguish between multiple targets with very close proximity in the delay-Doppler space, resulting in a resolution that substantially exceeds that of standard matched-filtering based techniques without introducing leakage effects inherent in recently proposed compressed sensing-based radar methods.

Connected through hyperedge HE-1 (induced by source paper "a general and yet efficient scheme for sub nyquist radar processing"):
  with members:
    - "a general sequential delay doppler estimation scheme for sub nyquist pulse doppler radar"
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "gridless quadrature compressive sampling with interpolated array technique"
    - "quadrature compressive sampling for radar signals"
    - "sub nyquist radar via doppler focusing"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-2 (induced by source paper "super resolution radar imaging via convex optimization"):
  with members:
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "analysis of sparse mimo radar"
    - "super resolution from noisy data"

Connected through hyperedge HE-3 (induced by source paper "a general sequential delay doppler estimation scheme for sub nyquist pulse doppler radar"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "gridless quadrature compressive sampling with interpolated array technique"
    - "quadrature compressive sampling for radar signals"
    - "sub nyquist radar via doppler focusing"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-4 (induced by source paper "super resolution mimo radar"):
  with members:
    - "a theory of super resolution from short time fourier transform measurements"
    - "refined analysis of sparse mimo radar"
    - "super resolution of positive sources the discrete setup"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "super resolution radar"
    - "atomic norm denoising with applications to line spectral estimation"
    - "identification of sparse linear operators"
    - "analysis of sparse mimo radar"

Connected through hyperedge HE-5 (induced by source paper "high resolution compressed sensing radar using difference set codes"):
  with members:
    - "sub nyquist radar via doppler focusing"
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "structured compressed sensing from theory to applications"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "mimo mc radar a mimo radar approach based on matrix completion"):
  with members:
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "target estimation in colocated mimo radar via matrix completion"
    - "mimo radar using compressive sampling"
    - "a geometric approach to low rank matrix completion"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "sampling and reconstruction of operators"):
  with members:
    - "cornerstones of sampling of operator theory"
    - "sampling of stochastic operators"
    - "identification of sparse linear operators"
    - "compressive identification of linear operators"
    - "the restricted isometry property for time frequency structured random matrices"

Connected through hyperedge HE-8 (induced by source paper "cornerstones of sampling of operator theory"):
  with members:
    - "sampling and reconstruction of operators"
    - "estimation of overspread scattering functions"
    - "sampling of stochastic operators"
    - "identification of sparse linear operators"
    - "the restricted isometry property for time frequency structured random matrices"

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)