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: "continuous analog channel estimation aided beamforming for massive mimo systems"
  Abstract: Analog beamforming is an attractive solution to reduce the implementation cost of massive antenna transceivers by using few up/down-conversion chains. However, reduction in the number of up/down-conversion chains leads to an increase in channel estimation (CE) overhead when conventional digital CE techniques are used. This paper introduces a novel CE technique called continuous analog channel estimation (CACE), that can be performed without digital processing, enables analog beamforming at the receiver and additionally provides resilience against oscillator phase-noise. In CACE a reference tone is transmitted continuously with the data signals, and the receiver uses the received signal for reference as a matched filter for combining the data signals, albeit via analog processing. By avoiding digital processing, the CE overhead with CACE is reduced significantly and furthermore becomes independent of the number of antenna elements. We propose a receiver architecture for CACE, analyze its performance in the presence of oscillator phase-noise, and derive near-optimal system parameters. Transmit beamforming and initial access procedure with CACE are also discussed. Simulations confirm that, in comparison to conventional CE, CACE suffers only a small loss in signal-to-interference-plus-noise-ratio, while providing phase-noise resilience and a significant reduction in the CE overhead.

Connected through hyperedge HE-1 (induced by source paper "optimal reference signal design for phase noise compensation in multi carrier massive mimo systems"):
  with members:
    - "periodic analog channel estimation aided beamforming for massive mimo systems"
    - "on the phase tracking reference signal pt rs design for 5g new radio nr"
    - "hybrid beamforming with selection for multiuser massive mimo systems"
    - "phase noise compensation for ofdm systems"
    - "impact of general channel aging conditions on the downlink performance of massive mimo"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "dynamic subarrays for hybrid precoding in wideband mmwave mimo systems"
    - "hybrid beamforming for massive mimo a survey"

Connected through hyperedge HE-2 (induced by source paper "reflections in the sky millimeter wave communication with uav carried intelligent reflectors"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "intelligent reflecting surface enhanced wireless network joint active and passive beamforming design"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "artificial neural networks based machine learning for wireless networks a tutorial"

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)