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: "beam alignment for millimetre wave links with motion prediction of autonomous vehicles"
  Abstract: Intelligent Transportation Systems (ITSs) require ultra-low end-to-end delays and multi-gigabit-per-second data transmission. Millimetre Waves (mmWaves) communications can fulfil these requirements. However, the increased mobility of Connected and Autonomous Vehicles (CAVs), requires frequent beamforming - thus introducing increased overhead. In this paper, a new beamforming algorithm is proposed able to achieve overhead-free beamforming training. Leveraging from the CAVs sensory data, broadcast with Dedicated Short Range Communications (DSRC) beacons, the position and the motion of a CAV can be estimated and beamform accordingly. To minimise the position errors, an analysis of the distinct error components was presented. The network performance is further enhanced by adapting the antenna beamwidth with respect to the position error. Our algorithm outperforms the legacy IEEE 802.11ad approach proving it a viable solution for the future ITS applications and services.

Connected through hyperedge HE-1 (induced by source paper "end to end simulation of 5g mmwave networks"):
  with members:
    - "integration of carrier aggregation and dual connectivity for the ns 3 mmwave module"
    - "x tcp a cross layer approach for tcp uplink flows in mmwave networks"
    - "enhancing tcp end to end performance in millimeter wave communications"
    - "tcp in 5g mmwave networks link level retransmissions and mp tcp"
    - "provisioning low latency resilient mobile edge clouds for 5g"
    - "caching meets millimeter wave communications for enhanced mobility management in 5g networks"
    - "reliable video streaming over mmwave with multi connectivity and network coding"
    - "improved handover through dual connectivity in 5g mmwave mobile networks"

Connected through hyperedge HE-2 (induced by source paper "multi radio 5g architecture for connected and autonomous vehicles application and design insights"):
  with members:
    - "decoding delay performance of random linear network coding for broadcast"
    - "agile calibration process of full stack simulation frameworks for v2x communications"
    - "modeling and design of millimeter wave networks for highway vehicular communication"
    - "mmwave system for future its a mac layer approach for v2x beam steering"
    - "wireless vehicular networks in emergencies a single frequency network approach"
    - "rethinking the intercept probability of random linear network coding"

Connected through hyperedge HE-3 (induced by source paper "mmwave system for future its a mac layer approach for v2x beam steering"):
  with members:
    - "millimeter wave channel modeling and cellular capacity evaluation"
    - "toward cloud based vehicular networks with efficient resource management"

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)