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 "analysis of frequency agile radar via compressed sensing"):
  Abstract: Frequency agile radar (FAR) is known to have excellent electronic counter-countermeasures performance and the potential to realize spectrum sharing in dense electromagnetic environments. Many compressed sensing (CS) based algorithms have been developed for joint range and Doppler estimation in FAR. This paper considers theoretical analysis of FAR via CS algorithms. In particular, we analyze the properties of the sensing matrix, which is a highly structured random matrix. We then derive bounds on the number of recoverable targets. Numerical simulations and field experiments validate the theoretical findings and demonstrate the effectiveness of CS approaches to FAR.

Includes members:
  Member "sparse doppler sensing based on nested arrays":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low rank toeplitz matrix estimation via random ultra sparse rulers")
  Member "spectrum sharing radar coexistence via xampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communications and radar coexistence in the massive mimo regime uplink analysis")
  Member "refined analysis of sparse mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")
  Member "fundamental limits of hrr profiling and velocity compensation for stepped frequency waveforms":
    also connected through other hyperedges:
      (none)
  Member "structured random measurements in signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications")
  Member "spatial compressive sensing for mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "serial quantization for sparse time sequences")
  Member "quadrature compressive sampling for radar signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a general and yet efficient scheme for sub nyquist radar processing")
  Member "sub nyquist radar via doppler focusing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain")

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)