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: "energy constrained uav trajectory design for ground node localization"
  Abstract: The use of aerial anchors for localizing terrestrial nodes has recently been recognized as a cost-effective, swift and flexible solution for better localization accuracy, providing localization services when the GPS is jammed or satellite reception is not possible. In this paper, the localization of terrestrial nodes when using mobile unmanned aerial vehicles (UAVs) as aerial anchors is presented. We propose a novel framework to derive localization error in urban areas. In contrast to the existing works, our framework includes height-dependent UAV to ground channel characteristics and a highly detailed UAV energy consumption model. This enables us to explore different tradeoffs and optimize UAV trajectory for minimum localization error. In particular, we investigate the impact of UAV altitude, hovering time, number of waypoints and path length through formulating an energy-constrained optimization problem. Our results show that increasing the hovering time decreases the localization error considerably at the cost of a higher energy consumption. To keep the localization error below 100m, shorter hovering is only possible when the path altitude and radius are optimized. For a constant hovering time of 5 seconds, tuning both parameters to their optimal values brings the localization error from 150m down to 65m with a power saving around 25%

Connected through hyperedge HE-1 (induced by source paper "backscatter data collection with unmanned ground vehicle mobility management and power allocation"):
  with members:
    - "resource allocation for solar powered uav communication systems"
    - "aerial anchors positioning for reliable rss based outdoor localization in urban environments"
    - "wirelessly powered backscatter communications waveform design and snr energy tradeoff"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "inference from randomized transmissions by many backscatter sensors"
    - "multistatic scatter radio sensor networks for extended coverage"
    - "full duplex backscatter interference networks based on time hopping spread spectrum"

Connected through hyperedge HE-2 (induced by source paper "localization in ultra narrow band iot networks design guidelines and tradeoffs"):
  with members:
    - "localization in long range ultra narrow band iot networks using rssi"

Connected through hyperedge HE-3 (induced by source paper "tutorial on uav a blue sky view on wireless communication"):
  with members:
    - "cellular connectivity for uavs network modeling performance analysis and design guidelines"
    - "comp in the sky uav placement and movement optimization for multi user communications"
    - "key technologies and system trade offs for detection and localization of amateur drones"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "aerial anchors positioning for reliable rss based outdoor localization in urban environments"
    - "an overview of 3gpp release 15 study on enhanced lte support for connected drones"
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "reshaping cellular networks for the sky the major factors and feasibility"

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)