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: "agile calibration process of full stack simulation frameworks for v2x communications"
  Abstract: Computer simulations and real-world car trials are essential to investigate the performance of Vehicle-to-Everything (V2X) networks. However, simulations are imperfect models of the physical reality and can be trusted only when they indicate agreement with the real-world. On the other hand, trials lack reproducibility and are subject to uncertainties and errors. In this paper, we will illustrate a case study where the interrelationship between trials, simulation, and the reality-of-interest is presented. Results are then compared in a holistic fashion. Our study will describe the procedure followed to macroscopically calibrate a full-stack network simulator to conduct high-fidelity full-stack computer simulations.

Connected through hyperedge HE-1 (induced by source paper "agile data offloading over novel fog computing infrastructure for cavs"):
  with members:
    - "a city scale its g5 network for next generation intelligent transportation systems design insights and challenges"
    - "modeling and design of millimeter wave networks for highway vehicular communication"
    - "analysis and optimization of sparse random linear network coding for reliable multicast services"
    - "reliable and efficient autonomous driving the need for heterogeneous vehicular networks"
    - "network coding meets multimedia a review"

Connected through hyperedge HE-2 (induced by source paper "a dataset of full stack its g5 dsrc communications over licensed and unlicensed bands using a large scale urban testbed"):
  with members:
    - "agile data offloading over novel fog computing infrastructure for cavs"
    - "compositional verification for autonomous systems with deep learning components"
    - "operating its g5 dsrc over unlicensed bands a city scale performance evaluation"

Connected through hyperedge HE-3 (induced by source paper "location anomalies detection for connected and autonomous vehicles"):
  with members:
    - "poster parallel implementation of the omnet inet framework for v2x communications"

Connected through hyperedge HE-4 (induced by source paper "multi radio 5g architecture for connected and autonomous vehicles application and design insights"):
  with members:
    - "beam alignment for millimetre wave links with motion prediction of autonomous vehicles"
    - "decoding delay performance of random linear network coding for broadcast"
    - "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-5 (induced by source paper "a city scale its g5 network for next generation intelligent transportation systems design insights and challenges"):
  with members:
    - "efficient v2v communication scheme for 5g mmwave hyper connected cavs"
    - "multi radio 5g architecture for connected and autonomous vehicles application and design insights"
    - "modeling and design of millimeter wave networks for highway vehicular communication"
    - "mmwave system for future its a mac layer approach for v2x beam steering"
    - "optimized certificate revocation list distribution for secure v2x communications"
    - "harbornet a real world testbed for vehicular networks"

Connected through hyperedge HE-6 (induced by source paper "poster parallel implementation of the omnet inet framework for v2x communications"):
  with members:
    - "multi radio 5g architecture for connected and autonomous vehicles application and design insights"

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.PF(Performance)