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: "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"
  Abstract: Millimeter wave (mmWave) has great potential in realizing high data rate thanks to the large spectral channels. It is considered as a key technology for the fifth generation wireless networks and is already used in wireless LAN (e.g., IEEE 802.11ad). Using mmWave for vehicular communications, however, is often viewed with some skepticism due to a misconception that the Doppler spread would become too large at these high frequencies. This is not true when directional beam is employed for communications. In this paper, closed form expressions relating the channel coherence time and beamwidth are derived. Unlike prior work that assumed perfect beam pointing, the pointing error due to the receiver motion is incorporated to show that there exists a non-zero optimal beamwidth that maximizes the coherence time. To investigate the mobility effect on the beam alignment which is an important feature in mmWave systems, a novel concept of beam coherence time is defined. The beam coherence time, which is an effective measure of beam alignment frequency, is shown to be much larger than the conventional channel coherence time and thus results in reduced beam alignment overhead. Using the derived correlation function, the channel coherence time, and the beam coherence time, an overall performance metric considering both the channel time-variation and the beam alignment overhead is derived. Using this metric, it is shown that beam alignment in every beam coherence time performs better than the beam alignment in every channel coherence time due to the large overhead for the latter case.

Connected through hyperedge HE-1 (induced by source paper "non uniform quantization codebook based hybrid precoding to reduce feedback overhead in millimeter wave mimo systems"):
  with members:
    - "downlink precoding with mixed statistical and imperfect instantaneous csi for massive mimo systems"
    - "channel feedback based on aod adaptive subspace codebook in fdd massive mimo systems"
    - "spectral and energy efficiency of multi pair massive mimo relay network with hybrid processing"
    - "pilot power allocation through user grouping in multi cell massive mimo systems"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "dynamic subarrays for hybrid precoding in wideband mmwave mimo systems"

Connected through hyperedge HE-2 (induced by source paper "fully partially connected hybrid beamforming architectures for mmwave mu mimo"):
  with members:
    - "hybrid precoding architecture for massive multiuser mimo with dissipation sub connected or fully connected structures"
    - "on the energy efficiency of mimo hybrid beamforming for millimeter wave systems with nonlinear power amplifiers"
    - "hybrid beamforming based on implicit channel state information for millimeter wave links"
    - "fully connected vs sub connected hybrid precoding architectures for mmwave mu mimo"
    - "millimeter wave mimo prototype measurements and experimental results"
    - "hybrid analog digital beamforming for massive mimo systems"
    - "frequency domain compressive channel estimation for frequency selective hybrid mmwave mimo systems"
    - "a robust time domain beam alignment scheme for multi user wideband mmwave systems"

Connected through hyperedge HE-3 (induced by source paper "active learning and csi acquisition for mmwave initial alignment"):
  with members:
    - "improved target acquisition rates with feedback codes"
    - "linear block coding for efficient beam discovery in millimeter wave communication networks"
    - "overview of millimeter wave communications for fifth generation 5g wireless networks with a focus on propagation models"
    - "comparative analysis of initial access techniques in 5g mmwave cellular networks"
    - "hybrid beamforming for massive mimo a survey"
    - "active learning from imperfect labelers"
    - "an efficient feedback coding scheme with low error probability for discrete memoryless channels"
    - "a simple proof for the optimality of randomized posterior matching"

Connected through hyperedge HE-4 (induced by source paper "deep cnn based channel estimation for mmwave massive mimo systems"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning in physical layer communications"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep learning for massive mimo csi feedback"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "low rank tensor decomposition aided channel estimation for millimeter wave mimo ofdm systems"

Connected through hyperedge HE-5 (induced by source paper "deep learning predictive band switching in wireless networks"):
  with members:
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "beam codebook design for 5g mmwave terminals"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "caching meets millimeter wave communications for enhanced mobility management in 5g networks"
    - "improved handover through dual connectivity in 5g mmwave mobile networks"
    - "millimeter wave beam selection using out of band spatial information"

Connected through hyperedge HE-6 (induced by source paper "second best beam alignment via bayesian multi armed bandits"):
  with members:
    - "active learning and csi acquisition for mmwave initial alignment"
    - "energy efficient interactive beam alignment for millimeter wave networks"
    - "coded energy efficient beam alignment for millimeter wave networks"
    - "optimal beam sweeping and communication in mobile millimeter wave networks"
    - "throughput optimal beam alignment in millimeter wave networks"
    - "efficient beam alignment in millimeter wave systems using contextual bandits"
    - "design and analysis of initial access in millimeter wave cellular networks"
    - "millimeter wave channel modeling and cellular capacity evaluation"

Connected through hyperedge HE-7 (induced by source paper "path loss models for v2v mmwave communication performance evaluation and open challenges"):
  with members:
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-8 (induced by source paper "deep learning coordinated beamforming for highly mobile millimeter wave systems"):
  with members:
    - "modeling and analyzing millimeter wave cellular systems"
    - "position aided beam alignment for millimeter wave backhaul systems with large phased arrays"
    - "base station diversity propagation measurements at 73 ghz millimeter wave for 5g coordinated multipoint comp analysis"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "intra cluster characteristics of 28 ghz wireless channel in urban micro street canyon"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave 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)