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: "system of systems modeling analysis and optimization of hybrid vehicular traffic"
  Abstract: While the development of fully autonomous vehicles is one of the major research fields in the Intelligent Transportation Systems (ITSs) domain, the upcoming long-term transition period – the hybrid vehicular traffic – is often neglected. However, within the next decades, automotive systems with heterogeneous autonomy levels will share the same road network, resulting in new problems for traffic management systems and communication network infrastructure providers. In this paper, we identify key challenges of the upcoming hybrid traffic scenario and present a system-of-systems model, which brings together approaches and methods from traffic modeling, data science, and communication engineering in order to allow data-driven traffic flow optimization. The proposed model consists of data acquisition, data transfer, data analysis, and data exploitation and exploits real world sensor data as well as simulative optimization methods. Based on the results of multiple case studies, which focus on individual challenges (e.g., resource-efficient data transfer and dynamic routing of vehicles), we point out approaches for using the existing infrastructure with a higher grade of efficiency.

Connected through hyperedge HE-1 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations"):
  with members:
    - "towards cooperative data rate prediction for future mobile and vehicular 6g networks"
    - "a reinforcement learning approach for efficient opportunistic vehicle to cloud data transfer"
    - "empirical analysis of client based network quality prediction in vehicular multi mno networks"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "toward intelligent vehicular networks a machine learning framework"
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"
    - "learning how to communicate in the internet of things finite resources and heterogeneity"

Connected through hyperedge HE-2 (induced by source paper "data driven network simulation for performance analysis of anticipatory vehicular communication systems"):
  with members:
    - "towards 6g networks use cases and technologies"
    - "empirical analysis of client based network quality prediction in vehicular multi mno networks"
    - "heterogeneous v2v communications in multi link and multi rat vehicular networks"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "toward intelligent vehicular networks a machine learning framework"
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "deep learning in physical layer communications"
    - "end to end learning of communications systems without a channel model"

Connected through hyperedge HE-3 (induced by source paper "lightweight simulation of hybrid aerial and ground based vehicular communication networks"):
  with members:
    - "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"
    - "lightweight joint simulation of vehicular mobility and communication with limosim"

Connected through hyperedge HE-4 (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"
    - "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"
    - "lightweight joint simulation of vehicular mobility and communication with limosim"
    - "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 "towards data driven simulation of end to end network performance indicators"):
  with members:
    - "empirical analysis of client based network quality prediction in vehicular multi mno networks"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "efficient machine type communication using multi metric context awareness for cars used as mobile sensors in upcoming 5g networks"
    - "end to end learning of communications systems without a channel model"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "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-6 (induced by source paper "empirical analysis of client based network quality prediction in vehicular multi mno networks"):
  with members:
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "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"
    - "b a t mobile leveraging mobility control knowledge for efficient routing in mobile robotic networks"
    - "kernel based adaptive online reconstruction of coverage maps with side information"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

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)