human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimum co design for spectrum sharing between matrix completion based mimo radars and a mimo communication system")
  Member "target estimation in colocated mimo radar via matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimum co design for spectrum sharing between matrix completion based mimo radars and a mimo communication system")
  Member "identification of parametric underspread linear systems and super resolution radar":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a general and yet efficient scheme for sub nyquist radar processing")
  Member "mimo radar using compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse blind deconvolution for distributed radar autofocus imaging")
  Member "a geometric approach to low rank matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a riemannian gossip approach to subspace learning on grassmann manifold")
  Member "the power of convex relaxation near optimal matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "matrix completion from a few entries":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "matrix completion with noise":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")

Question: Please tell me which class the source paper 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)