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: "target estimation in colocated mimo radar via matrix completion"
  Abstract: We consider a colocated MIMO radar scenario, in which the receive antennas forward their measurements to a fusion center. Based on the received data, the fusion center formulates a matrix which is then used for target parameter estimation. When the receive antennas sample the target returns at Nyquist rate, and assuming that there are more receive antennas than targets, the data matrix at the fusion center is low-rank. When each receive antenna sends to the fusion center only a small number of samples, along with the sample index, the receive data matrix has missing elements, corresponding to the samples that were not forwarded. Under certain conditions, matrix completion techniques can be applied to recover the full receive data matrix, which can then be used in conjunction with array processing techniques, e.g., MUSIC, to obtain target information. Numerical results indicate that good target recovery can be achieved with occupancy of the receive data matrix as low as 50%.

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"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "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 "active target localization using low rank matrix completion and unimodal regression"):
  with members:
    - "near optimal adaptive compressed sensing"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "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-3 (induced by source paper "mimo mc radar a mimo radar approach based on matrix completion"):
  with members:
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "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-4 (induced by source paper "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"):
  with members:
    - "mimo mc radar a mimo radar approach based on matrix completion"
    - "spatial compressive sensing for mimo radar"
    - "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)