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: "sub nyquist radar via doppler focusing"
  Abstract: We investigate the problem of a monostatic pulse-Doppler radar transceiver trying to detect targets, sparsely populated in the radar's unambiguous time-frequency region. Several past works employ compressed sensing (CS) algorithms to this type of problem, but either do not address sample rate reduction, impose constraints on the radar transmitter, propose CS recovery methods with prohibitive dictionary size, or perform poorly in noisy conditions. Here we describe a sub-Nyquist sampling and recovery approach called Doppler focusing which addresses all of these problems: it performs low rate sampling and digital processing, imposes no restrictions on the transmitter, and uses a CS dictionary with size which does not increase with increasing number of pulses P. Furthermore, in the presence of noise, Doppler focusing enjoys an SNR increase which scales linearly with P, obtaining good detection performance even at SNRs as low as -25dB. The recovery is based on the Xampling framework, which allows reducing the number of samples needed to accurately represent the signal, directly in the analog-to-digital conversion process. After sampling, the entire digital recovery process is performed on the low rate samples without having to return to the Nyquist rate. Finally, our approach can be implemented in hardware using a previously suggested Xampling prototype.

Connected through hyperedge HE-1 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain"):
  with members:
    - "a sampling framework for solving physics driven inverse source problems"
    - "on unlimited sampling"
    - "shift invariant and sampling spaces associated with the special affine fourier transform"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "frida fri based doa estimation for arbitrary array layouts"
    - "a theory of super resolution from short time fourier transform measurements"
    - "identification of linear time varying systems through waveform diversity"
    - "super resolution in phase space"

Connected through hyperedge HE-2 (induced by source paper "functional nonlinear sparse models"):
  with members:
    - "learning power spectrum maps from quantized power measurements"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "block iterative reweighted algorithms for super resolution of spectrally sparse signals"
    - "forward backward greedy algorithms for atomic norm regularization"
    - "computational complexity of certifying restricted isometry property"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "atomic norm denoising with applications to line spectral estimation"

Connected through hyperedge HE-3 (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"
    - "quadrature compressive sampling for radar signals"
    - "extended range profiling in stepped frequency radar with sparse recovery"

Connected through hyperedge HE-4 (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"
    - "quadrature compressive sampling for radar signals"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-5 (induced by source paper "sub nyquist sar via fourier domain range doppler processing"):
  with members:
    - "fast compressed sensing sar imaging based on approximated observation"
    - "recovery of sparse matrices via matrix sketching"
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "xampling at the rate of innovation"
    - "compressed beamforming in ultrasound imaging"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "sub nyquist sampling bridging theory and practice"

Connected through hyperedge HE-6 (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"
    - "quadrature compressive sampling for radar signals"
    - "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-7 (induced by source paper "sparsity based super resolution for sem images"):
  with members:
    - "image super resolution using deep convolutional networks"
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "compressed beamforming in ultrasound imaging"
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "sparsity based sub wavelength imaging with partially incoherent light via quadratic compressed sensing"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-8 (induced by source paper "stable support recovery of stream of pulses with application to ultrasound imaging"):
  with members:
    - "recovery of sparse positive signals on the sphere from low resolution measurements"
    - "super resolution on the sphere using convex optimization"
    - "sparse spikes deconvolution on thin grids"
    - "robust recovery of positive stream of pulses"
    - "robust recovery of stream of pulses using convex optimization"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "exact recovery of dirac ensembles from the projection onto spaces of spherical harmonics"
    - "exact recovery of non uniform splines from the projection onto spaces of algebraic polynomials"

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)