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 "ambiguity function of the transmit beamspace based mimo radar"):
  Abstract: In this paper, we derive an ambiguity function (AF) for the transmit beamspace (TB)-based MIMO radar for the case of far-field targets and narrow-band waveforms. The effects of transmit coherent processing gain and waveform diversity are incorporated into the AF definition. To cover all the phase information conveyed by different factors, we introduce the equivalent transmit phase centers. The newly defined AF serves as a generalized AF form for which the phased-array (PA) and traditional MIMO radar AFs are important special cases. We establish relationships among the defined TB-based MIMO radar AF and the existing AF results, including the Woodward's AF, the AFs defined for traditional colocated MIMO radar, and also the PA radar AF, respectively. Moreover, we compare the TB-based MIMO radar AF with the square-summation-form AF definition and identify two limiting cases to bound its “clear region” in Doppler-delay domain that is free of sidelobes. Corresponding bounds for these two cases are derived, and it is shown that the bound for the worst case is inversely proportional to the number of transmitted waveforms K, whereas the bound for the best case is independent of K. The actual “clear region” of the TB-based MIMO radar AF depends on the array configuration and is in between of the worst- and best-case bounds. We propose a new TB design strategy to reduce the levels of the AF sidelobes and show in simulations that proper design of the TB matrix leads to reduction of the relative sidelobe levels of the TB-based MIMO radar AF.

Includes members:
  Member "spatial compressive sensing for mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences")
  Member "efficient transmit beamspace design for search free based doa estimation in mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transmit array interpolation for doa estimation via tensor decomposition in 2 d mimo radar")
  Member "moving target parameters estimation in noncoherent mimo radar systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "suboptimum low complexity joint multi target detection and localization for noncoherent mimo radar with widely separated antennas")
  Member "transmit energy focusing for doa estimation in mimo radar with colocated antennas":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "machine learning based high resolution doa measurement and robust dm for hybrid analog digital massive mimo transceiver")
  Member "optimal joint target detection and parameter estimation by mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recursive distributed detection for composite hypothesis testing nonlinear observation models in additive gaussian noise")
  Member "mimo radar using compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse blind deconvolution for distributed radar autofocus imaging")
  Member "phased mimo radar a tradeoff between phased array and mimo radars":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast algorithms for designing unimodular waveform s with good correlation properties")
  Member "diversity integration tradeoffs in mimo detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient transmit beamspace design for search free based doa estimation in mimo radar")

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)