human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "boosting vehicle to cloud communication by machine learning enabled context prediction"):
  Abstract: The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. However, the massive increases in Machine-type Communication (MTC) highly stress the capacities of the network infrastructure. With the system-immanent limitation of resources in cellular networks and the resource competition between human cell users and MTC, more resource-efficient channel access methods are required in order to improve the coexistence of the different communicating entities. In this paper, we present a machine learning-enabled transmission scheme for client-side opportunistic data transmission. By considering the measured channel state as well as the predicted future channel behavior, delay-tolerant MTC is performed with respect to the anticipated resource-efficiency. The proposed mechanism is evaluated in comprehensive field evaluations in public Long Term Evolution (LTE) networks, where it is able to increase the mean data rate by 194% while simultaneously reducing the average power consumption by up to 54%.

Includes members:
  Member "empirical analysis of client based network quality prediction in vehicular multi mno networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")
  Member "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a reinforcement learning approach for efficient opportunistic vehicle to cloud data transfer")
  Member "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")
  Member "discover your competition in lte client based passive data rate prediction by machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards cooperative data rate prediction for future mobile and vehicular 6g networks")
  Member "connected vehicular transportation data analytics and traffic dependent networking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "data driven network simulation for performance analysis of anticipatory vehicular communication systems")
  Member "rushing full speed with lte advanced is economical a power consumption analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discover your competition in lte client based passive data rate prediction by machine learning")
  Member "energy efficient uav communication with trajectory optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint trajectory and resource allocation design for energy efficient secure uav communication systems")
  Member "b a t mobile leveraging mobility control knowledge for efficient routing in mobile robotic networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lightweight simulation of hybrid aerial and ground based vehicular communication networks")

Question: Please tell me which class the source paper 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)