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: "evolving boxes for fast vehicle detection"
  Abstract: We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to early discard unlikely regions; a fine-turning network produces detailed features for these candidate boxes. We show intriguingly that by applying different feature fusion techniques, the initial boxes can be refined for both localization and recognition. We evaluate our network on the recent DETRAC benchmark and obtain a significant improvement over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network achieves 9–13 FPS detection speed on a moderate commercial GPU.

Connected through hyperedge HE-1 (induced by source paper "spotnet self attention multi task network for object detection"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "road user detection in videos"
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "focal loss for dense object detection"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "dc spp yolo dense connection and spatial pyramid pooling based yolo for object detection"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "feature fused ssd fast detection for small objects"
    - "extend the shallow part of single shot multibox detector via convolutional neural network"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "enhancement of ssd by concatenating feature maps for object detection"
    - "r fcn 3000 at 30fps decoupling detection and classification"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (induced by source paper "online multi object tracking framework with the gmphd filter and occlusion group management"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "fusion of head and full body detectors for multi object tracking"
    - "real time multiple object tracking a study on the importance of speed"
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-4 (induced by source paper "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"):
  with members:
    - "using deep learning and google street view to estimate the demographic makeup of the us"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "compact bilinear pooling"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-5 (induced by source paper "deep affinity network for multiple object tracking"):
  with members:
    - "learning non uniform hypergraph for multi object tracking"
    - "deep cosine metric learning for person re identification"
    - "benchmark data and method for real time people counting in cluttered scenes using depth sensors"
    - "machine learning methods for solving assignment problems in multi target tracking"
    - "learning regression and verification networks for long term visual tracking"
    - "learning optimal parameters for multi target tracking with contextual interactions"
    - "fusion of head and full body detectors for multi object tracking"
    - "detect to track and track to detect"

Connected through hyperedge HE-6 (induced by source paper "precise temporal action localization by evolving temporal proposals"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "non local neural networks"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "cascaded boundary regression for temporal action detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "the kinetics human action video dataset"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-7 (induced by source paper "arbitrary oriented scene text detection via rotation proposals"):
  with members:
    - "deep matching prior network toward tighter multi oriented text detection"
    - "improving text proposals for scene images with fully convolutional networks"
    - "cascaded segmentation detection networks for word level text spotting"
    - "integrating scene text and visual appearance for fine grained image classification"
    - "detecting oriented text in natural images by linking segments"
    - "text flow a unified text detection system in natural scene images"
    - "character proposal network for robust text extraction"
    - "text attentional convolutional neural network for scene text detection"

Connected through hyperedge HE-8 (induced by source paper "bridge damage detection using single stage detector and field inspection images"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "transfer learning based crack detection by autonomous uavs"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "focal loss for dense object detection"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "an analysis of scale invariance in object detection snip"
    - "ssd single shot multibox detector"

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)