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: "detrac a new benchmark and protocol for multi object tracking"
  Abstract: In recent years, most effective multi-object tracking (MOT) methods are based on the tracking-by-detection framework. Existing performance evaluations of MOT methods usually separate the target association step from the object detection step by using the same object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effect of object detection accuracy to the overall MOT performance. This is based on a new large-scale DETection and tRACking (DETRAC) benchmark dataset. The DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140 thousand frames and 1.2 million labeled bounding boxes of objects) for both object detection and MOT. We evaluate complete MOT systems constructed from combinations of state-of-the-art target association methods and object detection schemes. Our analysis shows the complex effects of object detection accuracy on MOT performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and target association for comprehensive analysis.

Connected through hyperedge HE-1 (induced by source paper "video monitoring queries"):
  with members:
    - "approximate query processing using deep generative models"
    - "multi attribute selectivity estimation using deep learning"
    - "yolov3 an incremental improvement"
    - "blazeit fast exploratory video queries using neural networks"
    - "verdictdb universalizing approximate query processing"
    - "focus querying large video datasets with low latency and low cost"
    - "towards a hands free query optimizer through deep learning"
    - "the case for learned index structures"

Connected through hyperedge HE-2 (induced by source paper "end to end recurrent multi object tracking and trajectory prediction with relational reasoning"):
  with members:
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "deep sets"
    - "end to end representation learning for correlation filter based tracking"
    - "re3 real time recurrent regression networks for visual tracking of generic objects"

Connected through hyperedge HE-3 (induced by source paper "the unmanned aerial vehicle benchmark object detection and tracking"):
  with members:
    - "recurrent filter learning for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "simple online and realtime tracking with a deep association metric"
    - "drone based object counting by spatially regularized regional proposal network"
    - "ron reverse connection with objectness prior networks for object detection"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "okutama action an aerial view video dataset for concurrent human action detection"
    - "crest convolutional residual learning for visual tracking"

Connected through hyperedge HE-4 (induced by source paper "focal loss dense detector for vehicle surveillance"):
  with members:
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "loss max pooling for semantic image segmentation"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-5 (induced by source paper "towards lightweight convolutional neural networks for object detection"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dilated residual networks"
    - "coordinating filters for faster deep neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "imagenet pre trained models with batch normalization"
    - "hardware oriented approximation of convolutional neural networks"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-6 (induced by source paper "automatic ground truths projected image annotations for omnidirectional vision"):
  with members:
    - "a data set for evaluating the performance of multi class multi object video tracking"
    - "mot16 a benchmark for multi object tracking"
    - "3d reconstruction from full view fisheye camera"
    - "motchallenge 2015 towards a benchmark for multi target tracking"
    - "ua detrac a new benchmark and protocol for multi object detection and tracking"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "mot16 a benchmark for multi object tracking"):
  with members:
    - "fast r cnn"
    - "motchallenge 2015 towards a benchmark for multi target tracking"
    - "ua detrac a new benchmark and protocol for multi object detection and tracking"
    - "imagenet large scale visual recognition challenge"
    - "deformable part models are convolutional neural 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)