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: "rushing full speed with lte advanced is economical a power consumption analysis"
  Abstract: Boosting data rates in LTE mobile networks is one of the key features of LTE-Advanced. This improved user experience is achieved by Carrier Aggregation (CA), in which the available spectrum of an operator is bundled out of several frequency bands. Accordingly, the user equipment has to supply multiple reception chains and therefore consumes considerably more power during a transmission. On the other hand, transmissions terminate faster, which enables a quick switchover into energy-saving mode. In order to examine these opposed facts, empirical analyses of existing devices are first carried out. Subsequently, we present a new CA enhancement of an existing context- aware power consumption model which incorporates the development density of the environment and the mobile device mobility. Based on the extended model we perform a detailed power consumption analysis and show that CA leads to power savings of 31% if the data rate doubled for large file transmissions. In addition, we show that CA can lead to power savings even from a data rate increase of 25%, regardless of mobility and urban development density. Besides, the measurement results show that CA operated in the same band leads to a lower power consumption than inter-band CA.

Connected through hyperedge HE-1 (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 context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g 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"
    - "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-2 (induced by source paper "discover your competition in lte client based passive data rate prediction by machine learning"):
  with members:
    - "owl a reliable online watcher for lte control channel measurements"
    - "client based control channel analysis for connectivity estimation in lte networks"
    - "srslte an open source platform for lte evolution and experimentation"

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)