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: "using deep networks for drone detection"
  Abstract: Drone detection is the problem of finding the smallest rectangle that encloses the drone(s) in a video sequence. In this study, we propose a solution using an end-to-end object detection model based on convolutional neural networks. To solve the scarce data problem for training the network, we propose an algorithm for creating an extensive artificial dataset by combining background-subtracted real images. With this approach, we can achieve precision and recall values both of which are high at the same time.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "adversarial spatio temporal learning for video deblurring"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "video salient object detection via fully convolutional networks"
    - "how drones look crowdsourced knowledge transfer for aerial video saliency prediction"
    - "deep mutual learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"

Connected through hyperedge HE-2 (induced by source paper "aerial multi object tracking by detection using deep association networks"):
  with members:
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "online multi object tracking with dual matching attention networks"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "cornernet detecting objects as paired keypoints"
    - "deep cosine metric learning for person re identification"
    - "event based moving object detection and tracking"

Connected through hyperedge HE-3 (induced by source paper "sok security and privacy in the age of drones threats challenges solution mechanisms and scientific gaps"):
  with members:
    - "game of drones detecting streamed poi from encrypted fpv channel"
    - "dolphinattack inaudible voice commands"
    - "flight dynamics based recovery of a uav trajectory using ground cameras"
    - "dr0wned cyber physical attack with additive manufacturing"
    - "flying objects detection from a single moving camera"

Connected through hyperedge HE-4 (induced by source paper "drone detection using depth maps"):
  with members:
    - "vision based real time aerial object localization and tracking for uav sensing system"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "yolo9000 better faster stronger"
    - "flying objects detection from a single moving camera"

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)