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: "beamspace channel estimation in mmwave systems via cosparse image reconstruction technique"
  Abstract: This paper considers the beamspace channel estimation problem in 3D lens antenna array under a millimeter-wave communication system. We analyze the focusing capability of the 3D lens antenna array and the sparsity of the beamspace channel response matrix. Considering the analysis, we observe that the channel matrix can be treated as a 2D natural image; that is, the channel is sparse, and the changes between adjacent elements are subtle. Thus, for the channel estimation, we incorporate an image reconstruction technique called sparse non-informative parameter estimator-based cosparse analysis AMP for imaging (SCAMPI) algorithm. The SCAMPI algorithm is faster and more accurate than earlier algorithms such as orthogonal matching pursuit and support detection algorithms. To further improve the SCAMPI algorithm, we model the channel distribution as a generic Gaussian mixture (GM) probability and embed the expectation maximization learning algorithm into the SCAMPI algorithm to learn the parameters in the GM probability. We show that the GM probability outperforms the common uniform distribution used in image reconstruction. We also propose a phase-shifter-reduced selection network structure to decrease the power consumption of the system and prove that the SCAMPI algorithm is robust even if the number of phase shifters is reduced by 10%.

Connected through hyperedge HE-1 (induced by source paper "3 d positioning and environment mapping for mmwave communication systems"):
  with members:
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "uplink channel estimation and data transmission in millimeter wave cran with lens antenna arrays"
    - "csi based outdoor localization for massive mimo experiments with a learning approach"
    - "leveraging mmwave imaging and communications for simultaneous localization and mapping"
    - "direct localization for massive mimo"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "millimeter wave channel modeling and cellular capacity evaluation"

Connected through hyperedge HE-2 (induced by source paper "compressive sensing based channel estimation for millimeter wave full dimensional mimo with lens array"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "compressive sensing techniques for next generation wireless communications"
    - "multi user millimeter wave mimo with full dimensional lens antenna array"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"

Connected through hyperedge HE-3 (induced by source paper "deep learning based channel estimation for beamspace mmwave massive mimo systems"):
  with members:
    - "deep learning for wireless physical layer opportunities and challenges"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep residual learning for image recognition"
    - "from denoising to compressed sensing"

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)