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: "data driven network simulation for performance analysis of anticipatory vehicular communication systems"
  Abstract: The provision of reliable connectivity is envisioned as a key enabler for future autonomous driving. Anticipatory communication techniques have been proposed for proactively considering the properties of the highly dynamic radio channel within the communication systems themselves. Since real world experiments are highly time-consuming and lack a controllable environment, performance evaluations and parameter studies for novel anticipatory vehicular communication systems are typically carried out based on network simulations. However, due to the required simplifications and the wide range of unknown parameters (e.g., Mobile Network Operator (MNO)-specific configurations of the network infrastructure), the achieved results often differ significantly from the behavior in real world evaluations. In this paper, we present Data-driven Network Simulation (DDNS) as a novel data-driven approach for analyzing and optimizing anticipatory vehicular communication systems. Different machine learning models are combined for achieving a close to reality representation of the analyzed system's behavior. In a proof of concept evaluation focusing on opportunistic vehicular data transfer, the proposed method is validated against field measurements and system-level network simulation. In contrast to the latter, DDNS does not only provide massively faster result generation, it also achieves a significantly better representation of the real world behavior due to implicit consideration of cross-layer dependencies by the machine learning approach.

Connected through hyperedge HE-1 (induced by source paper "towards cooperative data rate prediction for future mobile and vehicular 6g networks"):
  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"
    - "falcon an accurate real time monitor for client based mobile network data analytics"
    - "wireless network intelligence at the edge"
    - "discover your competition in lte client based passive data rate prediction by machine learning"
    - "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)