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: "analysis of sparse mimo radar"
  Abstract: We consider a multiple-input-multiple-output radar system and derive a theoretical framework for the recoverability of targets in the azimuth-range domain and the azimuth-range-Doppler domain via sparse approximation algorithms. Using tools developed in the area of compressive sensing, we prove bounds on the number of detectable targets and the achievable resolution in the presence of additive noise. Our theoretical findings are validated by numerical simulations.

Connected through hyperedge HE-1 (induced by source paper "super resolution radar imaging via convex optimization"):
  with members:
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "super resolution from noisy data"
    - "identification of parametric underspread linear systems and super resolution radar"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing and parallel acquisition"):
  with members:
    - "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees"
    - "optimal sparse recovery for multi sensor measurements"
    - "wireless compressive sensing over fading channels with distributed sparse random projections"
    - "self calibration and biconvex compressive sensing"
    - "refined analysis of sparse mimo radar"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "multi view in lensless compressive imaging"
    - "wireless compressive sensing for energy harvesting sensor nodes"

Connected through hyperedge HE-3 (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"
    - "super resolution radar"
    - "atomic norm denoising with applications to line spectral estimation"
    - "identification of sparse linear operators"
    - "compressed sensing off the grid"

Connected through hyperedge HE-4 (induced by source paper "summer sub nyquist mimo radar"):
  with members:
    - "cognitive sub nyquist hardware prototype of a collocated mimo radar"
    - "mimo mc radar a mimo radar approach based on matrix completion"
    - "spatial compressive sensing for mimo radar"
    - "recovery of sparse matrices via matrix sketching"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"

Connected through hyperedge HE-5 (induced by source paper "refined analysis of sparse mimo radar"):
  with members:
    - "super resolution radar"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "measurement matrix design for compressive sensing based mimo radar"
    - "general deviants an analysis of perturbations in compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "recovery guarantees for multifrequency chirp waveforms in compressed radar sensing"):
  with members:
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "restricted isometries for partial random circulant matrices"

Connected through hyperedge HE-7 (induced by source paper "structured random measurements in signal processing"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "a partial derandomization of phaselift using spherical designs"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "convolutional compressed sensing using deterministic sequences"
    - "gespar efficient phase retrieval of sparse signals"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "phase retrieval from coded diffraction patterns"
    - "compressive multiplexing of correlated signals"

Connected through hyperedge HE-8 (induced by source paper "spatial compressive sensing for mimo radar"):
  with members:
    - "sub nyquist radar via doppler focusing"
    - "remote sensing via ell_1 minimization"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "a probabilistic and ripless theory of compressed sensing"
    - "mimo radar using compressive sampling"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "rank awareness in joint sparse recovery"

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)