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: "super resolution radar"
  Abstract: In this paper we study the identification of a time-varying linear system from its response to a known input signal. More specifically, we consider systems whose response to the input signal is given by a weighted superposition of delayed and Doppler shifted versions of the input. This problem arises in a multitude of applications such as wireless communications and radar imaging. Due to practical constraints, the input signal has finite bandwidth B, and the received signal is observed over a finite time interval of length T only. This gives rise to a delay and Doppler resolution of 1/B and 1/T. We show that this resolution limit can be overcome, i.e., we can exactly recover the continuous delay-Doppler pairs and the corresponding attenuation factors, by solving a convex optimization problem. This result holds provided that the distance between the delay-Doppler pairs is at least 2.37/B in time or 2.37/T in frequency. Furthermore, this result allows the total number of delay-Doppler pairs to be linear up to a log-factor in BT, the dimensionality of the response of the system, and thereby the limit for identifiability. Stated differently, we show that we can estimate the time-frequency components of a signal that is S-sparse in the continuous dictionary of time-frequency shifts of a random window function, from a number of measurements, that is linear up to a log-factor in S.

Connected through hyperedge HE-1 (induced by source paper "tree search network for sparse regression"):
  with members:
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "deep sparse coding using optimized linear expansion of thresholds"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "adaptive feature selection computationally efficient online sparse linear regression under rip"
    - "admm net a deep learning approach for compressive sensing mri"
    - "distributed compressive sensing a deep learning approach"

Connected through hyperedge HE-2 (induced by source paper "atomic norm denoising for complex exponentials with unknown waveform modulations"):
  with members:
    - "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"
    - "atomic norm minimization for modal analysis from random and compressed samples"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "generalized line spectral estimation via convex optimization"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"

Connected through hyperedge HE-3 (induced by source paper "off the grid recovery of time and frequency shifts with multiple measurement vectors"):
  with members:
    - "exploiting prior information in block sparse signals"
    - "distribution aware block sparse recovery via convex optimization"
    - "super resolution mimo radar"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "on the sensitivity of continuous time noncoherent fading channel capacity"

Connected through hyperedge HE-4 (induced by source paper "bayesian approach with extended support estimation for sparse regression"):
  with members:
    - "map support detection for greedy sparse signal recovery algorithms in compressive sensing"
    - "from denoising to compressed sensing"
    - "generalized orthogonal matching pursuit"
    - "rank awareness in joint sparse recovery"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "super resolution of complex exponentials from modulations with unknown waveforms"):
  with members:
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "self calibration and biconvex compressive sensing"
    - "super resolution of positive sources the discrete setup"
    - "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"

Connected through hyperedge HE-6 (induced by source paper "a super resolution framework for tensor decomposition"):
  with members:
    - "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"
    - "learning one hidden layer neural networks with landscape design"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "super resolution on the sphere using convex optimization"
    - "robust recovery of stream of pulses using convex optimization"
    - "nuclear norm of higher order tensors"
    - "provable tensor methods for learning mixtures of generalized linear models"

Connected through hyperedge HE-7 (induced by source paper "super resolution mimo radar"):
  with members:
    - "a theory of super resolution from short time fourier transform measurements"
    - "refined analysis of sparse mimo radar"
    - "super resolution of positive sources the discrete setup"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "atomic norm denoising with applications to line spectral estimation"
    - "identification of sparse linear operators"
    - "analysis of sparse mimo radar"
    - "compressed sensing off the grid"

Connected through hyperedge HE-8 (induced by source paper "exact dimensionality reduction for partial line spectra estimation problems"):
  with members:
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "robust recovery of stream of pulses using convex optimization"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"

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)