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: "quadrature compressive sampling for radar signals"
  Abstract: Quadrature sampling has been widely applied in coherent radar systems to extract in-phase and quadrature (  $I$   and   $Q$   ) components in the received radar signal. However, the sampling is inefficient because the received signal contains only a small number of significant target signals. This paper incorporates the compressive sampling (CS) theory into the design of the quadrature sampling system, and develops a quadrature compressive sampling (QuadCS) system to acquire the   $I$    and    $Q$   components with low sampling rate. The QuadCS system first randomly projects the received signal into a compressive bandpass signal and then utilizes the quadrature sampling to output compressive    $I$   and   $Q$   components. The compressive outputs are used to reconstruct the   $I$   and   $Q$    components. To understand the system performance, we establish the frequency domain representation of the QuadCS system. With the waveform-matched dictionary, we prove that the QuadCS system satisfies the restricted isometry property with overwhelming probability. For   $K$    target signals in the observation interval   $T$   , simulations show that the QuadCS requires just   ${\cal O}(K\log(BT/K))$   samples to stably reconstruct the signal, where    $B$   is the signal bandwidth. The reconstructed signal-to-noise ratio decreases by 3 dB for every octave increase in the target number    $K$   and increases by 3 dB for every octave increase in the compressive bandwidth. Theoretical analyses and simulations verify that the proposed QuadCS is a valid system to acquire the   $I$    and    $Q$   components in the received radar signals.

Connected through hyperedge HE-1 (induced by source paper "analysis of frequency agile radar via compressed sensing"):
  with members:
    - "sparse doppler sensing based on nested arrays"
    - "spectrum sharing radar coexistence via xampling"
    - "refined analysis of sparse mimo radar"
    - "fundamental limits of hrr profiling and velocity compensation for stepped frequency waveforms"
    - "structured random measurements in signal processing"
    - "spatial compressive sensing for mimo radar"
    - "sub nyquist radar via doppler focusing"
    - "extended range profiling in stepped frequency radar with sparse recovery"

Connected through hyperedge HE-2 (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"
    - "sub nyquist radar via doppler focusing"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-3 (induced by source paper "quadrature compressive sampling sar imaging"):
  with members:
    - "sub nyquist sar via fourier domain range doppler processing"

Connected through hyperedge HE-4 (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"
    - "sub nyquist radar via doppler focusing"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-5 (induced by source paper "gridless quadrature compressive sampling with interpolated array technique"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-6 (induced by source paper "segment sliding reconstruction of pulsed radar echoes with sub nyquist sampling"):
  with members:
    - "sparse recovery of streaming signals using l1 homotopy"
    - "sub nyquist radar via doppler focusing"
    - "xampling signal acquisition and processing in union of subspaces"
    - "segmented compressed sampling for analog to information conversion method and performance analysis"
    - "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)