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: "ua detrac a new benchmark and protocol for multi object detection and tracking"
  Abstract: In recent years, numerous effective multi-object tracking (MOT) methods are developed because of the wide range of applications. Existing performance evaluations of MOT methods usually separate the object tracking step from the object detection step by using the same fixed object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140,000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and object tracking methods. Our analysis shows the complex effects of object detection accuracy on MOT system performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and object tracking for comprehensive analysis.

Connected through hyperedge HE-1 (induced by source paper "trained model fusion for object detection using gating network"):
  with members:
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"
    - "yolov3 an incremental improvement"
    - "visual saliency prediction using a mixture of deep neural networks"
    - "partial adversarial domain adaptation"
    - "edf ensemble distill and fuse for easy video labeling"
    - "robust deep multi modal learning based on gated information fusion network"
    - "rank of experts detection network ensemble"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (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-3 (induced by source paper "drone based rgbt vehicle detection and counting a challenge"):
  with members:
    - "nwpu crowd a large scale benchmark for crowd counting"
    - "locality constrained spatial transformer network for video crowd counting"
    - "perspective guided convolution networks for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "mrcnet crowd counting and density map estimation in aerial and ground imagery"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "vision meets drones a challenge"
    - "the unmanned aerial vehicle benchmark object detection and tracking"

Connected through hyperedge HE-4 (induced by source paper "tracking road users using constraint programming"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "tracking without bells and whistles"
    - "multiple object tracking in urban traffic scenes with a multiclass object detector"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "multiple object tracking with kernelized correlation filters in urban mixed traffic"
    - "performance measures and a data set for multi target multi camera tracking"
    - "high speed tracking with kernelized correlation filters"
    - "motchallenge 2015 towards a benchmark for multi target tracking"

Connected through hyperedge HE-5 (induced by source paper "mots multi object tracking and segmentation"):
  with members:
    - "panoptic segmentation"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "how transferable are the datasets collected by active learners"
    - "practical obstacles to deploying active learning"
    - "combined image and world space tracking in traffic scenes"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-6 (induced by source paper "benchmarking robustness in object detection autonomous driving when winter is coming"):
  with members:
    - "d 2 city a large scale dashcam video dataset of diverse traffic scenarios"
    - "rain removal in traffic surveillance does it matter"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "fixing the train test resolution discrepancy"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "semantic foggy scene understanding with synthetic data"

Connected through hyperedge HE-7 (induced by source paper "learning multi object tracking and segmentation from automatic annotations"):
  with members:
    - "video instance segmentation"
    - "disentangling monocular 3d object detection"
    - "seamless scene segmentation"
    - "mots multi object tracking and segmentation"
    - "panoptic deeplab"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "combined image and world space tracking in traffic scenes"

Connected through hyperedge HE-8 (induced by source paper "inserting videos into videos"):
  with members:
    - "video to video synthesis"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "recycle gan unsupervised video retargeting"
    - "artistic style transfer for videos and spherical images"
    - "yolov3 an incremental improvement"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "multimodal unsupervised image to image translation"
    - "context aware synthesis and placement of object instances"

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)