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: "yolo lite a real time object detection algorithm optimized for non gpu computers"
  Abstract: This paper focuses on YOLO-LITE, a real-time object detection model developed to run on portable devices such as a laptop or cellphone lacking a Graphics Processing Unit (GPU). The model was first trained on the PASCAL VOC dataset then on the COCO dataset, achieving a mAP of 33.81% and 12.26% respectively. YOLO-LITE runs at about 21 FPS on a non-GPU computer and 10 FPS after implemented onto a website with only 7 layers and 482 million FLOPS. This speed is 3.8x faster than the fastest state of art model, SSD MobilenetvI. Based on the original object detection algorithm YOLOV2, YOLO- LITE was designed to create a smaller, faster, and more efficient model increasing the accessibility of real-time object detection to a variety of devices.

Connected through hyperedge HE-1 (induced by source paper "penet object detection using points estimation in aerial images"):
  with members:
    - "clustered object detection in aerial images"
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "objects as points"
    - "augmentation for small object detection"
    - "vision meets drones a challenge"
    - "cornernet detecting objects as paired keypoints"
    - "detnet a backbone network for object detection"
    - "the unmanned aerial vehicle benchmark object detection and tracking"

Connected through hyperedge HE-2 (induced by source paper "traffic signs detection and recognition system using deep learning"):
  with members:
    - "yolo9000 better faster stronger"
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "expandnets linear over parameterization to train compact convolutional networks"):
  with members:
    - "acnet strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "learning deep representations with probabilistic knowledge transfer"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-4 (induced by source paper "efficientdet scalable and efficient object detection"):
  with members:
    - "searching for mobilenetv3"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learning data augmentation strategies for object detection"
    - "rethinking imagenet pre training"
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-5 (induced by source paper "expandnets exploiting linear redundancy to train small networks"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "yolov3 an incremental improvement"
    - "learning deep representations with probabilistic knowledge transfer"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "compression aware training of deep 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.CV(Computer Vision and Pattern Recognition)