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: "key technologies and system trade offs for detection and localization of amateur drones"
  Abstract: The use of amateur drones is expected to significantly increase over the upcoming years. However, regulations do not allow such drones to fly over all areas, in addition to typical altitude limitations. As a result, there is an urgent need for amateur drone surveillance solutions. These solutions should include means of accurate detection, classification, and localization of the unwanted drones in a no-fly zone. In this article, we give an overview of promising techniques for modulation classification and signal-strength-based localization of amateur drones by using surveillance drones. By introducing a generic altitude-dependent propagation model, we show how detection and localization performance depend on the altitude of surveillance drones. Particularly, our simulation results show a 25 dB reduction in the minimum detectable power or 10 times coverage enhancement of a surveillance drone by flying at the optimum altitude. Moreover, for a target no-fly zone, the location estimation error of an amateur drone can be remarkably reduced by optimizing the positions of the surveillance drones. Finally, we conclude the article with a general discussion about the future work and possible challenges in aerial surveillance systems.

Connected through hyperedge HE-1 (induced by source paper "cellular connectivity for uavs network modeling performance analysis and design guidelines"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "cellular uav to uav communications"
    - "a brief history on the theoretical analysis of dense small cell wireless networks"
    - "supporting uav cellular communications through massive mimo"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "mobile networks connected drones field trials simulations and design insights"
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"

Connected through hyperedge HE-2 (induced by source paper "pinch an effective efficient and robust solution to drone detection via network traffic analysis"):
  with members:
    - "machine learning based delay aware uav detection and operation mode identification over encrypted wi fi traffic"
    - "flight dynamics based recovery of a uav trajectory using ground cameras"
    - "device fingerprinting in wireless networks challenges and opportunities"

Connected through hyperedge HE-3 (induced by source paper "picking a needle in a haystack detecting drones via network traffic analysis"):
  with members:
    - "device fingerprinting in wireless networks challenges and opportunities"

Connected through hyperedge HE-4 (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"
    - "energy constrained uav trajectory design for ground node localization"
    - "comp in the sky uav placement and movement optimization for multi user communications"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "energy constrained uav trajectory design for ground node localization"):
  with members:
    - "aerial anchors positioning for reliable rss based outdoor localization in urban environments"
    - "energy efficient uav communication with trajectory optimization"

Connected through hyperedge HE-6 (induced by source paper "coexistence of terrestrial and aerial users in cellular networks"):
  with members:
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "downlink coverage analysis for a finite 3d wireless network of unmanned aerial vehicles"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "coverage maximization for a poisson field of drone cells"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "designing and implementing future aerial communication networks"
    - "a tractable approach to coverage and rate in cellular networks"

Connected through hyperedge HE-7 (induced by source paper "coverage maximization for a poisson field of drone cells"):
  with members:
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "dynamic base station repositioning to improve performance of drone small cells"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "dynamic base station repositioning to improve spectral efficiency of drone small cells"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "designing and implementing future aerial communication networks"
    - "a tractable approach to coverage and rate in cellular networks"

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)