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: "delay and doppler spreads of nonstationary vehicular channels for safety relevant scenarios"
  Abstract: Vehicular communication channels are characterized by a nonstationary time-frequency-selective fading process due to rapid changes in the environment. The nonstationary fading process can be characterized by assuming local stationarity for a region with finite extent in time and frequency. For this finite region, the wide-sense stationarity and uncorrelated scattering assumption approximately holds, and we are able to calculate a time-frequency-dependent local scattering function (LSF). In this paper, we estimate the LSF from a large set of measurements collected in the DRIVEWAY'09 measurement campaign, which focuses on scenarios for intelligent transportation systems (ITSs). We then obtain the time-frequency-varying power delay profile (PDP) and the time-frequency-varying Doppler power spectral density (DSD) from the LSF. Based on the PDP and the DSD, we analyze the time-frequency-varying root-mean-square (RMS) delay spread and the RMS Doppler spread. We show that the distribution of these channel parameters follows a bimodal Gaussian mixture distribution. High RMS delay spread values are observed in situations with rich scattering, whereas high RMS Doppler spreads are obtained in drive-by scenarios.

Connected through hyperedge HE-1 (induced by source paper "toward intelligent vehicular networks a machine learning framework"):
  with members:
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "machine learning for vehicular networks"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "vehicular communications a network layer perspective"

Connected through hyperedge HE-2 (induced by source paper "a time variant channel prediction and feedback framework for interference alignment"):
  with members:
    - "on the degrees of freedom for opportunistic interference alignment with 1 bit feedback the 3 cell case"
    - "grassmannian differential limited feedback for interference alignment"
    - "adaptive interference alignment with csi uncertainty"
    - "ensemble properties of rvq based limited feedback beamforming codebooks"
    - "optimization of training and feedback overhead for beamforming over block fading channels"
    - "interference alignment with analog channel state feedback"
    - "interference alignment under limited feedback for mimo interference channels"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"

Connected through hyperedge HE-3 (induced by source paper "vehicular communications a physical layer perspective"):
  with members:
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "otfs orthogonal time frequency space"
    - "vehicular communications survey and challenges of channel and propagation models"
    - "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"
    - "a survey on device to device communication in cellular networks"
    - "what will 5g be"
    - "spatially sparse precoding in millimeter wave mimo systems"

Connected through hyperedge HE-4 (induced by source paper "orthogonal precoding for ultra reliable wireless communication links"):
  with members:
    - "performance characterization of a real time massive mimo system with los mobile channels"
    - "low complexity equalization for orthogonal time and frequency signaling otfs"
    - "analysis of discrete time mimo ofdm based orthogonal time frequency space modulation"
    - "low complexity modem structure for ofdm based orthogonal time frequency space modulation"
    - "otfs orthogonal time frequency space"
    - "time and frequency varying k factor of non stationary vehicular channels for safety relevant scenarios"
    - "diversity of mmse mimo receivers"

Connected through hyperedge HE-5 (induced by source paper "adaptive pilot patterns for ca ofdm systems in nonstationary wireless channels"):
  with members:
    - "waveform and spectrum management for unmanned aerial systems beyond 2025"
    - "pilot pattern adaptation for 5g mu mimo wireless communications"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"

Connected through hyperedge HE-6 (induced by source paper "low complexity equalization for orthogonal time and frequency signaling otfs"):
  with members:
    - "otfs orthogonal time frequency space"
    - "diversity of mmse mimo receivers"

Connected through hyperedge HE-7 (induced by source paper "measurement based wideband analysis of dynamic multipath propagation in vehicular communication scenarios"):
  with members:
    - "tracking of wideband multipath components in a vehicular communication scenario"

Connected through hyperedge HE-8 (induced by source paper "vehicular communications survey and challenges of channel and propagation models"):
  with members:
    - "a measurement based shadow fading model for vehicle to vehicle network simulations"
    - "tvr tall vehicle relayingin vehicular networks"
    - "geometry based vehicle to vehicle channel modeling for large scale simulation"

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.NI(Networking and Internet Architecture)