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: "lightweight joint simulation of vehicular mobility and communication with limosim"
  Abstract: The provision of reliable and efficient communication is a key requirement for the deployment of autonomous cars as well as for future Intelligent Transportation Systems (ITSs) in smart cities. Novel communications technologies will have to face highly-complex and extremely dynamic network topologies in a Vehicle-to-Everything (V2X)-context and will require the consideration of mobility information into decision processes for routing, handover and resource allocation. Consequently, researches and developers require simulation tools that are capable of providing realistic representations for both components as well as means for leveraging the convergence of mobility and communication. In this paper, we present a lightweight framework for the simulation of vehicular mobility, which has a communications-oriented perspective by design and is intended to be used in combination with a network simulator. In contrast to existing approaches, it works without requiring Interprocess Communication (IPC) using an integrated approach and is therefore able to reduce the complexity of simulation setups significantly. Since mobility and communication share the same codebase, it is able to model scenarios with a high level of interdependency between those two components. In a proof-of-concept study, we evaluate the proposed simulator in different example scenarios in an Long Term Evolution (LTE)-context using real-world map data.

Connected through hyperedge HE-1 (induced by source paper "lightweight simulation of hybrid aerial and ground based vehicular communication networks"):
  with members:
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "performance analysis of c v2x mode 4 communication introducing an open source c v2x simulator"
    - "performance evaluation and optimization of b a t m a n v routing for aerial and ground based mobile ad hoc networks"
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "flynetsim an open source synchronized uav network simulator based on ns 3 and ardupilot"
    - "efficient machine type communication using multi metric context awareness for cars used as mobile sensors in upcoming 5g networks"
    - "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"

Connected through hyperedge HE-2 (induced by source paper "unmanned aerial vehicles in logistics efficiency gains and communication performance of hybrid combinations of ground and aerial vehicles"):
  with members:
    - "lightweight simulation of hybrid aerial and ground based vehicular communication networks"
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "uav communications for 5g and beyond recent advances and future trends"
    - "performance analysis of c v2x mode 4 communication introducing an open source c v2x simulator"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "b a t mobile leveraging mobility control knowledge for efficient routing in mobile robotic networks"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

Connected through hyperedge HE-3 (induced by source paper "system of systems modeling analysis and optimization of hybrid vehicular traffic"):
  with members:
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"
    - "connected vehicular transportation data analytics and traffic dependent networking"
    - "b a t mobile leveraging mobility control knowledge for efficient routing in mobile robotic networks"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

Connected through hyperedge HE-4 (induced by source paper "performance evaluation and optimization of b a t m a n v routing for aerial and ground based mobile ad hoc networks"):
  with members:
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "a simple scheme for distributed passive load balancing in mobile ad hoc networks"
    - "b a t mobile leveraging mobility control knowledge for efficient routing in mobile robotic networks"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

Connected through hyperedge HE-5 (induced by source paper "the automat cvim a scalable data model for automotive big data marketplaces"):
  with members:
    - "car to cloud communication traffic analysis based on the common vehicle information model"
    - "novel common vehicle information model cvim for future automotive vehicle big data marketplaces"

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)