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: "channel covariance estimation in massive mimo frequency division duplex systems"
  Abstract: Channel covariance is emerging as a critical ingredient of the acquisition of instantaneous channel state information (CSI) in multi-user Massive MIMO systems operating in frequency division duplex (FDD) mode. In this context, channel reciprocity does not hold, and it is generally expected that covariance information about the downlink channel must be estimated and fed back by the user equipment (UE). As an alternative CSI acquisition technique, we propose to infer the downlink covariance based on the observed uplink covariance. This inference process relies on a dictionary of uplink/downlink covariance matrices, and on interpolation in the corresponding Riemannian space; once the dictionary is known, the estimation does not rely on any form of feedback from the UE. In this article, we present several variants of the interpolation method, and benchmark them through simulations.

Connected through hyperedge HE-1 (induced by source paper "uplink downlink channel covariance transformations and precoding design for fdd massive mimo"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "5g new radio unveiling the essentials of the next generation wireless access technology"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "deep learning for joint source channel coding of text"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "on the asymptotic equivalence of circulant and toeplitz matrices"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-2 (induced by source paper "passive radar at the roadside unit to configure millimeter wave vehicle to infrastructure links"):
  with members:
    - "an approximate message passing framework for side information"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "spatial channel covariance estimation for the hybrid mimo architecture a compressive sensing based approach"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "millimeter wave beam selection using out of band spatial information"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "hybrid beamforming for massive mimo a survey"

Connected through hyperedge HE-3 (induced by source paper "overcoming the channel estimation barrier in massive mimo communication systems"):
  with members:
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "an introduction to deep learning for the physical layer"
    - "deep learning for massive mimo csi feedback"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"

Connected through hyperedge HE-4 (induced by source paper "hybrid processing design for multipair massive mimo relaying with channel spatial correlation"):
  with members:
    - "secure massive mimo systems with limited rf chains"
    - "spectral and energy efficiency of multi pair massive mimo relay network with hybrid processing"
    - "framework of channel estimation for hybrid analog and digital processing enabled massive mimo communications"
    - "massive mimo with imperfect channel covariance information"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "hybrid beamforming for massive mimo a survey"
    - "hybrid mimo architectures for millimeter wave communications phase shifters or switches"
    - "massive mimo relaying with hybrid processing"

Connected through hyperedge HE-5 (induced by source paper "spatial covariance estimation for millimeter wave hybrid systems using out of band information"):
  with members:
    - "spatial channel covariance estimation for the hybrid mimo architecture a compressive sensing based approach"
    - "overview of full dimension mimo in lte advanced pro"
    - "millimeter wave beam selection using out of band spatial information"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-6 (induced by source paper "csi based outdoor localization for massive mimo experiments with a learning approach"):
  with members:
    - "multi band covariance interpolation with applications in massive mimo"
    - "modern wlan fingerprinting indoor positioning methods and deployment challenges"
    - "fingerprinting based positioning in distributed massive mimo systems"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-7 (induced by source paper "millimeter wave beam selection using out of band spatial information"):
  with members:
    - "robust location aided beam alignment in millimeter wave massive mimo"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "beamforming tradeoffs for initial ue discovery in millimeter wave mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"

Connected through hyperedge HE-8 (induced by source paper "hybrid precoding for massive mimo systems in cloud ran architecture with capacity limited fronthauls"):
  with members:
    - "layered downlink precoding for c ran systems with full dimensional mimo"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "on the number of rf chains and phase shifters and scheduling design with hybrid analog digital beamforming"
    - "hybrid beamforming for massive mimo a survey"
    - "regularized zf in cooperative broadcast channels under distributed csit a large system analysis"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "fronthaul constrained cloud radio access networks insights and challenges"

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)