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: "millimeter wave vehicular communication to support massive automotive sensing"
  Abstract: As driving becomes more automated, vehicles are being equipped with more sensors generating even higher data rates. Radars (RAdio Detection and Ranging) are used for object detection, visual cameras as virtual mirrors, and LIDARs (LIght Detection and Ranging) for generating high resolution depth associated range maps, all to enhance the safety and efficiency of driving. Connected vehicles can use wireless communication to exchange sensor data, allowing them to enlarge their sensing range and improve automated driving functions. Unfortunately, conventional technologies, such as dedicated short-range communication (DSRC) and 4G cellular communication, do not support the gigabit-per-second data rates that would be required for raw sensor data exchange between vehicles. This paper makes the case that millimeter wave (mmWave) communication is the only viable approach for high bandwidth connected vehicles. The motivations and challenges associated with using mmWave for vehicle-to-vehicle and vehicle-to-infrastructure applications are highlighted. A high-level solution to one key challenge - the overhead of mmWave beam training - is proposed. The critical feature of this solution is to leverage information derived from the sensors or DSRC as side information for the mmWave communication link configuration. Examples and simulation results show that the beam alignment overhead can be reduced by using position information obtained from DSRC.

Connected through hyperedge HE-1 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks"):
  with members:
    - "adaptive millimeter wave communications exploiting mobility and blockage dynamics"
    - "second best beam alignment via bayesian multi armed bandits"
    - "active learning and csi acquisition for mmwave initial alignment"
    - "energy efficient interactive beam alignment for millimeter wave networks"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "online learning for position aided millimeter wave beam training"
    - "beam training and data transmission optimization in millimeter wave vehicular networks"
    - "optimal beam sweeping and communication in mobile millimeter wave networks"

Connected through hyperedge HE-2 (induced by source paper "adaptive millimeter wave communications exploiting mobility and blockage dynamics"):
  with members:
    - "mobility and blockage aware communications in millimeter wave vehicular networks"
    - "second best beam alignment via bayesian multi armed bandits"
    - "energy efficient interactive beam alignment for millimeter wave networks"
    - "online learning for position aided millimeter wave beam training"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "beam training and data transmission optimization in millimeter wave vehicular networks"
    - "optimal beam sweeping and communication in mobile millimeter wave networks"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"

Connected through hyperedge HE-3 (induced by source paper "the disruptions of 5g on data driven technologies and applications"):
  with members:
    - "an e2e simulator for 5g nr networks"
    - "federated machine learning concept and applications"
    - "blockchain goes green an analysis of blockchain on low power nodes"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-4 (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-5 (induced by source paper "an efficient requirement aware attachment policy for future millimeter wave vehicular networks"):
  with members:
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "toward intelligent vehicular networks a machine learning framework"
    - "user association in massive mimo hetnets"
    - "hybrid spectrum sharing in mmwave cellular networks"
    - "initial access in 5g mmwave cellular networks"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "millimeter wave channel modeling and cellular capacity evaluation"

Connected through hyperedge HE-6 (induced by source paper "channel estimation for orthogonal time frequency space otfs massive mimo"):
  with members:
    - "mimo otfs in high doppler fading channels signal detection and channel estimation"
    - "orthogonal time frequency space modulation"
    - "otfs a new generation of modulation addressing the challenges of 5g"
    - "embedded pilot aided channel estimation for otfs in delay doppler channels"
    - "on otfs modulation for high doppler fading channels"
    - "interference cancellation and iterative detection for orthogonal time frequency space modulation"
    - "a simple two stage equalizer with simplified orthogonal time frequency space modulation over rapidly time varying channels"
    - "low complexity modem structure for ofdm based orthogonal time frequency space modulation"

Connected through hyperedge HE-7 (induced by source paper "client selection for federated learning with heterogeneous resources in mobile edge"):
  with members:
    - "federated learning with non iid data"
    - "differentially private federated learning a client level perspective"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "joint subcarrier and cpu time allocation for mobile edge computing"
    - "communication efficient learning of deep networks from decentralized data"
    - "deep residual learning for image recognition"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"
    - "millimeter wave channel modeling and cellular capacity evaluation"

Connected through hyperedge HE-8 (induced by source paper "lte and millimeter waves for v2i communications an end to end performance comparison"):
  with members:
    - "end to end simulation of 5g mmwave networks"
    - "performance study of lte and mmwave in vehicle to network communications"
    - "coverage and connectivity analysis of millimeter wave vehicular networks"
    - "millimeter wave interference avoidance via building aware associations"
    - "modeling and design of millimeter wave networks for highway vehicular communication"
    - "performance impact of los and nlos transmissions in dense cellular networks"
    - "millimeter wave cellular wireless networks potentials 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)