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: "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
  Abstract: While cars were only considered as means of personal transportation for a long time, they are currently transcending to mobile sensor nodes that gather highly up-to-date information for crowdsensing-enabled big data services in a smart city context. Consequently, upcoming 5G communication networks will be confronted with massive increases in Machine-type Communication (MTC) and require resource-efficient transmission methods in order to optimize the overall system performance and provide interference-free coexistence with human data traffic that is using the same public cellular network. In this paper, we bring together mobility prediction and machine learning based channel quality estimation in order to improve the resource-efficiency of car-to-cloud data transfer by scheduling the transmission time of the sensor data with respect to the anticipated behavior of the communication context. In a comprehensive field evaluation campaign, we evaluate the proposed context-predictive approach in a public cellular network scenario where it is able to increase the average data rate by up to 194% while simultaneously reducing the mean uplink power consumption by up to 54%.

Connected through hyperedge HE-1 (induced by source paper "a reinforcement learning approach for efficient opportunistic vehicle to cloud data transfer"):
  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"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

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"
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "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"
    - "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:
    - "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"
    - "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 "boosting vehicle to cloud communication by machine learning enabled context prediction"):
  with members:
    - "empirical analysis of client based network quality prediction in vehicular multi mno networks"
    - "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"
    - "discover your competition in lte client based passive data rate prediction by machine learning"
    - "connected vehicular transportation data analytics and traffic dependent networking"
    - "rushing full speed with lte advanced is economical a power consumption analysis"
    - "energy efficient uav communication with trajectory optimization"
    - "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"

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"
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "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:
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "system of systems modeling analysis and optimization of hybrid vehicular traffic"):
  with members:
    - "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 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-8 (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:
    - "lightweight joint simulation of vehicular mobility and communication with limosim"
    - "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"

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)