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 mimo radar"
  Abstract: A multiple input, multiple output (MIMO) radar emits probings signals with multiple transmit antennas and records the reflections from targets with multiple receive antennas. Estimating the relative angles, delays, and Doppler shifts from the received signals allows to determine the locations and velocities of the targets. Standard approaches to MIMO radar based on digital matched filtering or compressed sensing only resolve the angle-delay-Doppler triplets on a (1/(N T N R ), 1/B, 1/T ) grid, where N T  and N R  are the number of transmit and receive antennas, B is the bandwidth of the probing signals, and T is the length of the time interval over which the reflections are observed. In this work, we show that the continuous angle-delay-Doppler triplets and the corresponding attenuation factors can be recovered perfectly by solving a convex optimization problem. This result holds provided that the angle-delay-Doppler triplets are separated either by 10/(N T N R  − 1) in angle, 10.01/B in delay, or 10.01/T in Doppler direction. Furthermore, this result is optimal (up to log factors) in the number of angle-delay-Doppler triplets that can be recovered.

Connected through hyperedge HE-1 (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"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "super resolution radar"
    - "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-2 (induced by source paper "network assisted d2d relay selection under the presence of dynamic obstacles"):
  with members:
    - "improving the coverage and spectral efficiency of millimeter wave cellular networks using device to device relays"
    - "on the relay fallback tradeoff in millimeter wave wireless system"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "analysis of blockage effects on urban cellular networks"

Connected through hyperedge HE-3 (induced by source paper "blind two dimensional super resolution and its performance guarantee"):
  with members:
    - "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"
    - "self calibration and biconvex compressive sensing"
    - "super resolution on the sphere using convex optimization"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "the recoverability limit for superresolution via sparsity"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"

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)