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 "summer sub nyquist mimo radar"):
  Abstract: Multiple input multiple output (MIMO) radar exhibits several advantages with respect to traditional radar array systems in terms of flexibility and performance. However, MIMO radar poses new challenges for both hardware design and digital processing. In particular, achieving high azimuth resolution requires a large number of transmit and receive antennas. In addition, the digital processing is performed on samples of the received signal, from each transmitter to each receiver, at its Nyquist rate, which can be prohibitively large when high resolution is needed. Overcoming the rate bottleneck, sub-Nyquist sampling methods have been proposed that break the link between radar signal bandwidth and sampling rate. In this work, we extend these methods to MIMO configurations and propose a sub-Nyquist MIMO radar (SUMMeR) system that performs both time and spatial compression. We present a range-azimuth-Doppler recovery algorithm from sub-Nyquist samples obtained from a reduced number of transmitters and receivers, that exploits the sparsity of the recovered targets' parameters. This allows us to achieve reduction in the number of deployed antennas and the number of samples per receiver, without degrading the time and spatial resolutions. Simulations illustrate the detection performance of SUMMeR for different compression levels and shows that both time and spatial resolution are preserved, with respect to classic Nyquist MIMO configurations. We also examine the impact of design parameters, such as antennas' locations and carrier frequencies, on the detection performance, and provide guidelines for their choice.

Includes members:
  Member "cognitive sub nyquist hardware prototype of a collocated mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dictionary learning for adaptive gpr landmine classification")
  Member "mimo mc radar a mimo radar approach based on matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimum co design for spectrum sharing between matrix completion based mimo radars and a mimo communication system")
  Member "spatial compressive sensing for mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "serial quantization for sparse time sequences")
  Member "recovery of sparse matrices via matrix sketching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "localized compression applying convolutional neural networks to compressed images")
  Member "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimum co design for spectrum sharing between matrix completion based mimo radars and a mimo communication system")
  Member "fourier domain beamforming the path to compressed ultrasound imaging":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse convolutional beamforming for ultrasound imaging")
  Member "analysis of sparse mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "super resolution radar imaging via convex optimization")
  Member "compressed beamforming in ultrasound imaging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "serial quantization for sparse time sequences")

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)