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: "non uniform linear antenna array design and optimization for millimeter wave communications"
  Abstract: In this paper, we investigate the optimization of non-uniform linear antenna arrays (NULAs) for millimeter-wave (mmWave) line-of-sight (LoS) multiple-input multiple-output (MIMO) channels. Our focus is on the maximization of the system  effective multiplexing gain  (EMG), by optimizing the individual antenna positions in the transmit/receive NULAs. Here, the EMG is defined as the number of signal streams that are practically supported by the channel at a finite signal-to-noise ratio. We first derive analytical expressions for the asymptotic channel eigenvalues with arbitrarily deployed NULAs when, asymptotically, the end-to-end distance is sufficiently large compared with the aperture sizes of the transmit/receive NULAs. Based on the derived expressions, we prove that the asymptotically optimal NULA deployment that maximizes the achievable EMG should follow the groupwise Fekete-point distribution. Specifically, the antennas should be physically grouped into    $K$    separate ULAs with the minimum feasible antenna spacing within each ULA, where    $K$    is the target EMG to be achieved; in addition, the centers of these    $K$    ULAs follow the Fekete-point distribution. We numerically verify the asymptotic optimality of such an NULA deployment and extend it to a  groupwise projected arch-type  NULA deployment, which provides a more practical option for mmWave LoS MIMO systems with realistic non-asymptotic configurations. Numerical examples are provided to demonstrate a significant capacity gain of the optimized NULAs over traditional ULAs.

Connected through hyperedge HE-1 (induced by source paper "millimeter wave beamformed full dimensional mimo channel estimation based on atomic norm minimization"):
  with members:
    - "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"
    - "atomic norm denoising based joint channel estimation and faulty antenna detection for massive mimo"
    - "millimeter wave mimo channel estimation based on adaptive compressed sensing"
    - "super resolution delay doppler estimation for ofdm passive radar"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "exact joint sparse frequency recovery via optimization methods"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

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)