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: "massive mimo channel estimation for millimeter wave systems via matrix completion"
  Abstract: Millimeter wave (mmWave) massive multiple input multiple output (MIMO) systems realizing directive beamforming require reliable estimation of the wireless propagation channel. However, mmWave channels are characterized by high variability that severely challenges their recovery over short training periods. Current channel estimation techniques exploit either the channel sparsity in the beamspace domain or its low-rank property in the antenna domain, nevertheless, they still require large numbers of training symbols for the satisfactory performance. In this letter, we present a novel channel estimation algorithm that jointly exploits the latter two properties of mmWave channels to provide more accurate recovery, especially for shorter training intervals. The proposed iterative algorithm is based on the alternating direction method of multipliers and provides the global optimum solution to the considered convex mmWave channel estimation problem with fast convergence properties.

Connected through hyperedge HE-1 (induced by source paper "principal component analysis based broadband hybrid precoding for millimeter wave massive mimo systems"):
  with members:
    - "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"
    - "periodic analog channel estimation aided beamforming for massive mimo systems"
    - "device to device communications enabled energy efficient multicast scheduling in mmwave small cells"
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "wideband hybrid precoding for next generation backhaul fronthaul based on mmwave fd mimo"
    - "hybrid beamforming with selection for multiuser massive mimo systems"
    - "stochastic successive convex optimization for two timescale hybrid precoding in massive mimo"
    - "machine learning based hybrid precoding for mmwave mimo ofdm with dynamic subarray"

Connected through hyperedge HE-2 (induced by source paper "continuous analog channel estimation aided beamforming for massive mimo systems"):
  with members:
    - "periodic analog channel estimation aided beamforming for massive mimo systems"
    - "hybrid beamforming with selection for multiuser massive mimo systems"
    - "position aided beam alignment for millimeter wave backhaul systems with large phased arrays"
    - "omnidirectional precoding and combining based synchronization for millimeter wave massive mimo systems"
    - "design and analysis of initial access in millimeter wave cellular networks"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "initial access in 5g mmwave cellular networks"
    - "timing and carrier synchronization in wireless communication systems a survey and classification of research in the last 5 years"

Connected through hyperedge HE-3 (induced by source paper "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces"):
  with members:
    - "deep learning for ul dl channel calibration in generic massive mimo systems"
    - "towards smart and reconfigurable environment intelligent reflecting surface aided wireless network"
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "achievable rate maximization by passive intelligent mirrors"
    - "csi based outdoor localization for massive mimo experiments with a learning approach"
    - "beyond massive mimo the potential of positioning with large intelligent surfaces"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "decentralized consensus optimization based on parallel random walk"):
  with members:
    - "asynchronous distributed admm for large scale optimization part i algorithm and convergence analysis"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo 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)