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 "towards data driven simulation of end to end network performance indicators"):
  Abstract: Novel vehicular communication methods are mostly analyzed simulatively or analytically as real world performance tests are highly time-consuming and cost-intense. Moreover, the high number of uncontrollable effects makes it practically impossible to reevaluate different approaches under the exact same conditions. However, as these methods massively simplify the effects of the radio environment and various cross-layer interdependencies, the results of end-to-end indicators (e.g., the resulting data rate) often differ significantly from real world measurements. In this paper, we present a data-driven approach that exploits a combination of multiple machine learning methods for modeling the end-to-end behavior of network performance indicators within vehicular networks. The proposed approach can be exploited for fast and close to reality evaluation and optimization of new methods in a controllable environment as it implicitly considers cross-layer dependencies between measurable features. Within an example case study for opportunistic vehicular data transfer, the proposed approach is validated against real world measurements and a classical system-level network simulation setup. Although the proposed method does only require a fraction of the computation time of the latter, it achieves a significantly better match with the real world evaluations.

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 "system of systems modeling analysis and optimization of hybrid vehicular traffic":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")
  Member "boosting vehicle to cloud communication by machine learning enabled context prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "a reinforcement learning approach for efficient opportunistic vehicle to cloud data transfer")
  Member "efficient machine type communication using multi metric context awareness for cars used as mobile sensors in upcoming 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lightweight simulation of hybrid aerial and ground based vehicular communication networks")
  Member "end to end learning of communications systems without a channel model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep energy autoencoder for noncoherent multicarrier mu simo systems")
  Member "power of deep learning for channel estimation and signal detection in ofdm systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  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)