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: "mimo mc radar a mimo radar approach based on matrix completion"
  Abstract: In a typical multiple-input and multiple-output (MIMO) radar scenario, the receive nodes transmit to a fusion center either samples of the target returns, or the results of matched filtering with the transmit waveforms. Based on the data it receives from multiple antennas, the fusion center formulates a matrix, referred to as the data matrix, which, via standard array processing schemes leads to target detection and parameter estimation. In this paper, it is shown that under certain conditions, the data matrix is low rank and thus can be recovered based on knowledge of a small subset of its entries via matrix completion (MC) techniques. Leveraging the low-rank property of the data matrix, we propose a new MIMO radar approach, termed, MIMO-MC radar, in which each receive node either performs matched filtering with a small number of randomly selected dictionary waveforms or obtains sub-Nyquist samples of the target returns at random sampling instants, and forwards the results to a fusion center. Based on the received samples, and with knowledge of the sampling scheme, the fusion center partially fills the data matrix and subsequently applies MC techniques to estimate the full matrix. MIMO-MC radars share the advantages of MIMO radars with compressive sensing, (MIMO-CS), i.e., high resolution with reduced amounts of data, but unlike MIMO-CS radars do not require grid discretization. The MIMO-MC radar concept is illustrated through a uniform linear array configuration, and its target estimation performance is demonstrated via simulations.

Connected through hyperedge HE-1 (induced by source paper "optimum co design for spectrum sharing between matrix completion based mimo radars and a mimo communication system"):
  with members:
    - "overlapped mimo radar waveform design for coexistence with communication systems"
    - "spatial compressive sensing for mimo radar"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "target estimation in colocated mimo radar via matrix completion"
    - "complex valued random vectors and channels entropy divergence and capacity"
    - "mimo radar using compressive sampling"
    - "dynamic resource allocation in cognitive radio networks"
    - "matrix completion with noise"

Connected through hyperedge HE-2 (induced by source paper "summer sub nyquist mimo radar"):
  with members:
    - "cognitive sub nyquist hardware prototype of a collocated mimo radar"
    - "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"
    - "analysis of sparse mimo radar"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"

Connected through hyperedge HE-3 (induced by source paper "cognitive sub nyquist hardware prototype of a collocated mimo radar"):
  with members:
    - "spatial compressive sensing for mimo radar"
    - "recovery of sparse matrices via matrix sketching"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "sub nyquist radar via doppler focusing"
    - "mimo radar using compressive sampling"

Connected through hyperedge HE-4 (induced by source paper "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"):
  with members:
    - "spatial compressive sensing for mimo radar"
    - "target estimation in colocated mimo radar via matrix completion"
    - "measurement matrix design for compressive sensing based mimo radar"
    - "mimo radar using compressive sampling"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

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)