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: "achieving phase coherency and gain stability in active antenna arrays for sub 6 ghz fdd and tdd fd mimo challenges and solutions"
  Abstract: Massive MIMO has been the subject of intense interest in both academia and industry for the past few years. 3GPP standardization for cellular systems have adopted the principles of massive MIMO and categorized the use of large rectangular planar arrays at the base station as full-dimension MIMO (FD-MIMO) to operate in both TDD and FDD. Operating a large antenna array base station requires the system to overcome several implementation challenges caused by hardware impairments making practical solutions non-ideal and expensive to deploy at scale. It is important to learn from existing challenges and solutions in order to prepare for larger scale deployment for example with cell free massive MIMO. Hence in this paper, we specifically study the phase and amplitude instability due to RF impairments using measurements carried out in the lab and in the field in a commercial LTE network. We investigate the effect of phase and magnitude errors on the performance of FD-MIMO systems. We discuss and characterize various sources creating these errors including time varying phase drift from low cost local oscillator (LO) and internal temperature variations affecting frequency response of RF chains. The minimum requirements and tradeoffs of different LO architectures and calibration mechanisms for practical cellular deployment are discussed. We then provide details of a novel coherent LO distribution mechanism and related novel array calibration mechanism that can be applied to both TDD and FDD systems. Measurement results are provided to validate the performance of these methods used in a 2D full-connected hybrid beamforming array architecture called High Definition Active Antenna System (HDAAS). These results showcase the efficacy of the proposed methods which can easily be extended to other array architectures including sub-array hybrid beamforming and element-level digitization.

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)