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: "over sampling codebook based hybrid minimum sum mean square error precoding for millimeter wave 3d mimo"
  Abstract: Hybrid precoding design is challenging for millimeter-wave (mmWave) massive MIMO. Most prior hybrid precoding schemes are designed to maximize the sum spectral efficiency (SSE), while seldom investigate the bit-error-rate (BER). Therefore, we propose an over-sampling codebook (OSC)-based hybrid minimum sum-mean-square-error (min-SMSE) precoding scheme for mmWave multi-user three-dimensional (3D)-MIMO systems to optimize the BER, where multi-user transmission with multiple data streams for each user is considered. Specifically, given the effective baseband channel consisting of the real channel and analog precoding, we first design the digital precoder/combiner based on min-SMSE criterion to optimize the BER. To further reduce the SMSE between the transmit and receive signals, we propose an OSC-based joint analog precoder/combiner (JAPC) design. Simulation results show that the proposed scheme can achieve the better performance than its conventional counterparts.

Connected through hyperedge HE-1 (induced by source paper "data driven deep learning to design pilot and channel estimator for massive mimo"):
  with members:
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "compressive sensing based channel estimation for millimeter wave full dimensional mimo with lens array"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning in physical layer communications"
    - "compressive sensing techniques for next generation wireless communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep learning for massive mimo csi feedback"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"

Connected through hyperedge HE-2 (induced by source paper "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"):
  with members:
    - "wideband hybrid precoding for next generation backhaul fronthaul based on mmwave fd mimo"
    - "stochastic successive convex optimization for two timescale hybrid precoding in massive mimo"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "compressive sensing techniques for next generation wireless communications"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "hierarchical codebook design for beamforming training in millimeter wave communication"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-3 (induced by source paper "beam codebook design for 5g mmwave terminals"):
  with members:
    - "statistical blockage modeling and robustness of beamforming in millimeter wave systems"
    - "millimeter wave mimo prototype measurements and experimental results"
    - "hierarchical codebook design for beamforming training in millimeter wave communication"
    - "codebook design for millimeter wave channel estimation with hybrid precoding structure"
    - "beamforming tradeoffs for initial ue discovery in millimeter wave mimo systems"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "what will 5g be"
    - "designing unimodular codes via quadratic 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)