human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "concentration of measure inequalities for toeplitz matrices with applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "technical report observability with random observations")
  Member "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "noise folding in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "xampling signal acquisition and processing in union of subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast compressed power spectrum estimation towards a practical solution for wideband spectrum sensing")
  Member "segmented compressed sampling for analog to information conversion method and performance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gridless quadrature compressive sampling with interpolated array technique")
  Member "beyond nyquist efficient sampling of sparse bandlimited signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")

Question: Please tell me which class the source paper 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)