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: "vision based real time aerial object localization and tracking for uav sensing system"
  Abstract: The paper focuses on the problem of vision-based obstacle detection and tracking for unmanned aerial vehicle navigation. A real-time object localization and tracking strategy from monocular image sequences is developed by effectively integrating the object detection and tracking into a dynamic Kalman model. At the detection stage, the object of interest is automatically detected and localized from a saliency map computed via the image background connectivity cue at each frame; at the tracking stage, a Kalman filter is employed to provide a coarse prediction of the object state, which is further refined via a local detector incorporating the saliency map and the temporal information between two consecutive frames. Compared to existing methods, the proposed approach does not require any manual initialization for tracking, runs much faster than the state-of-the-art trackers of its kind, and achieves competitive tracking performance on a large number of image sequences. Extensive experiments demonstrate the effectiveness and superior performance of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "reconstruction of 3d flight trajectories from ad hoc camera networks"):
  with members:
    - "flight dynamics based recovery of a uav trajectory using ground cameras"
    - "geometric models of rolling shutter cameras"

Connected through hyperedge HE-2 (induced by source paper "object detection with convolutional neural networks"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "vision meets drones a challenge"
    - "deep learning for generic object detection a survey"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "yolov3 an incremental improvement"
    - "quantization mimic towards very tiny cnn for object detection"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"

Connected through hyperedge HE-3 (induced by source paper "adaptively denoising proposal collection for weakly supervised object localization"):
  with members:
    - "stacked wasserstein autoencoder"
    - "direct visual inertial odometry with semi dense mapping"
    - "unsupervised deep feature transfer for low resolution image classification"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "towards learning affine invariant representations via data efficient cnns"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "learning depth from single images with deep neural network embedding focal length"
    - "soft nms improving object detection with one line of code"

Connected through hyperedge HE-4 (induced by source paper "unsupervised deep feature transfer for low resolution image classification"):
  with members:
    - "stacked wasserstein autoencoder"
    - "adaptively denoising proposal collection for weakly supervised object localization"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "deep clustering for unsupervised learning of visual features"
    - "learning depth from single images with deep neural network embedding focal length"
    - "invariant information clustering for unsupervised image classification and segmentation"

Connected through hyperedge HE-5 (induced by source paper "bobby2 buffer based robust high speed object tracking"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "distractor aware siamese networks for visual object tracking"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "enkcf ensemble of kernelized correlation filters for high speed object tracking"
    - "fast online object tracking and segmentation a unifying approach"
    - "end to end representation learning for correlation filter based tracking"
    - "super convergence very fast training of residual networks using large learning rates"
    - "fully convolutional siamese networks for object tracking"

Connected through hyperedge HE-6 (induced by source paper "adaptive fusion for rgb d salient object detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "agile amulet real time salient object detection with contextual attention"
    - "rgbd salient object detection via deep fusion"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep contrast learning for salient object detection"
    - "visual saliency based on multiscale deep features"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "drone detection using depth maps"):
  with members:
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "using deep networks for drone detection"
    - "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)