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: "cellular network traces towards 5g usage analysis and generation"
  Abstract: Deployment and demand traces are a crucial tool to study today’s LTE systems, as well as their evolution toward 5G. In this paper, we use a set of real-world, crowdsourced traces, coming from the WeFi and OpenSignal apps, to investigate how present-day networks are deployed, and the load they serve. Given this information, we present a way to generate  synthetic  deployment and demand profiles, retaining the same features of their real-world counterparts. We further discuss a methodology using traces (both real-world and synthetic) to assess (i) to which extent the current deployment is adequate to the current  and future  demand, and (ii) the effectiveness of the existing strategies to improve network capacity. Applying our methodology to real-world traces, we find that present-day LTE deployments consist of multiple, entangled, medium- to large-sized cells. Furthermore, although today’s LTE networks are overprovisioned when compared to the present traffic demand, they will need substantial capacity improvements in order to face the load increase foreacasted between now and 2020.

Connected through hyperedge HE-1 (induced by source paper "from megabits to cpu ticks enriching a demand trace in the age of mec"):
  with members:
    - "the price of fog a data driven study on caching architectures in vehicular networks"
    - "a sharing and competition aware framework for cellular network evolution planning"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"
    - "efficient multi user computation offloading for mobile edge cloud computing"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "data for development the d4d challenge on mobile phone data"

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"
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "deep learning in physical layer communications"

Connected through hyperedge HE-3 (induced by source paper "planning uav activities for efficient user coverage in disaster areas"):
  with members:
    - "uav base station location optimization for next generation wireless networks overview and future research directions"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "drone small cells in the clouds design deployment and performance analysis"
    - "enabling disaster resilient 4g mobile communication networks"

Connected through hyperedge HE-4 (induced by source paper "machine learning at the edge a data driven architecture with applications to 5g cellular networks"):
  with members:
    - "a survey on mobile edge computing the communication perspective"
    - "fog computing based radio access networks issues and challenges"
    - "collaborative mobile edge computing in 5g networks new paradigms scenarios and challenges"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"
    - "anticipatory mobile computing a survey of the state of the art and research challenges"
    - "algorithms for enhanced inter cell interference coordination eicic in lte hetnets"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "millimeter wave cellular wireless networks potentials and challenges"

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)