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: "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
  Abstract: It was recently shown that low rank matrix completion theory can be employed for designing new sampling schemes in the context of MIMO radars, which can lead to the reduction of the high volume of data typically required for accurate target detection and estimation. Employing random samplers at each reception antenna, a partially observed version of the received data matrix is formulated at the fusion center, which, under certain conditions, can be recovered using convex optimization. This paper presents the theoretical analysis regarding the performance of matrix completion in colocated MIMO radar systems, exploiting the particular structure of the data matrix. Both Uniform Linear Arrays (ULAs) and arbitrary 2-dimensional arrays are considered for transmission and reception. Especially for the ULA case, under some mild assumptions on the directions of arrival of the targets, it is explicitly shown that the coherence of the data matrix is both asymptotically and approximately optimal with respect to the number of antennas of the arrays involved and further, the data matrix is recoverable using a subset of its entries with minimal cardinality. Sufficient conditions guaranteeing low matrix coherence and consequently satisfactory matrix completion performance are also presented, including the arbitrary 2-dimensional array case.

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:
    - "mimo mc radar a mimo radar approach based on matrix completion"
    - "overlapped mimo radar waveform design for coexistence with communication systems"
    - "spatial compressive sensing for mimo radar"
    - "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"
    - "mimo mc radar a mimo radar approach based on matrix completion"
    - "spatial compressive sensing for mimo radar"
    - "recovery of sparse matrices via matrix sketching"
    - "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 "active target localization using low rank matrix completion and unimodal regression"):
  with members:
    - "near optimal adaptive compressed sensing"
    - "target estimation in colocated mimo radar via matrix completion"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "matrix completion with noise"

Connected through hyperedge HE-4 (induced by source paper "cognitive sub nyquist hardware prototype of a collocated mimo radar"):
  with members:
    - "mimo mc radar a mimo radar approach based on matrix completion"
    - "spatial compressive sensing for mimo radar"
    - "recovery of sparse matrices via matrix sketching"
    - "sub nyquist radar via doppler focusing"
    - "mimo radar using compressive sampling"

Connected through hyperedge HE-5 (induced by source paper "mimo mc radar a mimo radar approach based on matrix completion"):
  with members:
    - "target estimation in colocated mimo radar via matrix completion"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "mimo radar using compressive sampling"
    - "a geometric approach to low rank matrix completion"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"):
  with members:
    - "sparse signal recovery by ell_q minimization under restricted isometry property"
    - "doa estimation in partially correlated noise using low rank sparse matrix decomposition"
    - "calibration using matrix completion with application to ultrasound tomography"
    - "new ric bounds via l_q minimization with 0 q 1 in compressed sensing"
    - "on ric bounds of compressed sensing matrices for approximating sparse solutions using l q quasi norms"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "matrix completion with noise"
    - "sparse recovery by non convex optimization instance optimality"

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)