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: "dronet efficient convolutional neural network detector for real time uav applications"
  Abstract: Unmanned Aerial Vehicles (drones) are emerging as a promising technology for both environmental and infrastructure monitoring, with broad use in a plethora of applications. Many such applications require the use of computer vision algorithms in order to analyse the information captured from an on-board camera. Such applications include detecting vehicles for emergency response and traffic monitoring. This paper therefore, explores the trade-offs involved in the development of a single-shot object detector based on deep convolutional neural networks (CNNs) that can enable UAVs to perform vehicle detection under a resource constrained environment such as in a UAV. The paper presents a holistic approach for designing such systems; the data collection and training stages, the CNN architecture, and the optimizations necessary to efficiently map such a CNN on a lightweight embedded processing platform suitable for deployment on UAVs. Through the analysis we propose a CNN architecture that is capable of detecting vehicles from aerial UAV images and can operate between 5–18 frames-per-second for a variety of platforms with an overall accuracy of ∼ 95%. Overall, the proposed architecture is suitable for UAV applications, utilizing low-power embedded processors that can be deployed on commercial UAVs.

Connected through hyperedge HE-1 (induced by source paper "edgenet balancing accuracy and performance for edge based convolutional neural network object detectors"):
  with members:
    - "yolov3 an incremental improvement"
    - "efficient convnet based object detection for unmanned aerial vehicles by selective tile processing"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "mask r cnn"
    - "projectionnet learning efficient on device deep networks using neural projections"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "deploying deep neural networks in the embedded space"):
  with members:
    - "f cnn text x a toolflow for mapping multiple convolutional neural networks on fpgas"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
    - "approximate fpga based lstms under computation time constraints"
    - "cascadecnn pushing the performance limits of quantisation"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "fpgaconvnet a toolflow for mapping diverse convolutional neural networks on embedded fpgas"
    - "mask r cnn"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-3 (induced by source paper "efficient convnet based object detection for unmanned aerial vehicles by selective tile processing"):
  with members:
    - "yolo9000 better faster stronger"

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.CV(Computer Vision and Pattern Recognition)