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 "millimeter wave beamformed full dimensional mimo channel estimation based on atomic norm minimization"):
  Abstract: The millimeter-wave (mmWave) full-dimensional (FD) MIMO system employs planar arrays at both the base station and user equipment and can simultaneously support both azimuth and elevation beamforming. In this paper, we propose atomic-norm-based methods for mmWave FD-MIMO channel estimation under both uniform planar arrays (UPA) and non-uniform planar arrays (NUPA). Unlike existing algorithms such as compressive sensing (CS) or subspace methods, the atomic-norm-based algorithms do not require to discretize the angle spaces of the angle of arrival (AoA) and angle of departure (AoD) into grids, thus provide much better accuracy in estimation. In the UPA case, to reduce the computational complexity, the original large-scale 4D atomic norm minimization problem is approximately reformulated as a semi-definite program (SDP) containing two decoupled two-level Toeplitz matrices. The SDP is then solved via the alternating direction method of multipliers (ADMM) where each iteration involves only closed-form computations. In the NUPA case, the atomic-norm-based formulation for channel estimation becomes nonconvex and a gradient-decent-based algorithm is proposed to solve the problem. Simulation results show that the proposed algorithms achieve better performance than the CS-based and subspace-based algorithms.

Includes members:
  Member "approximate support recovery of atomic line spectral estimation a tale of resolution and precision":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resolution limit for line spectral estimation theory and algorithm")
  Member "atomic norm denoising based joint channel estimation and faulty antenna detection for massive mimo":
    also connected through other hyperedges:
      (none)
  Member "millimeter wave mimo channel estimation based on adaptive compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beam discovery using linear block codes for millimeter wave communication networks")
  Member "super resolution delay doppler estimation for ofdm passive radar":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi dimensional spectral super resolution with prior knowledge via frequency selective vandermonde decomposition and admm")
  Member "non uniform linear antenna array design and optimization for millimeter wave communications":
    also connected through other hyperedges:
      (none)
  Member "compressed sensing based multi user millimeter wave systems how many measurements are needed":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi dimensional spectral super resolution with prior knowledge via frequency selective vandermonde decomposition and admm")
  Member "exact joint sparse frequency recovery via optimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")

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.SY(Systems and Control)