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: "on the stability of video detection and tracking"
  Abstract: In this paper, we study an important yet less explored aspect in video detection and tracking -- stability. Surprisingly, there is no prior work that tried to study it. As a result, we start our work by proposing a novel evaluation metric for video detection which considers both stability and accuracy. For accuracy, we extend the existing accuracy metric mean Average Precision (mAP). For stability, we decompose it into three terms: fragment error, center position error, scale and ratio error. Each error represents one aspect of stability. Furthermore, we demonstrate that the stability metric has low correlation with accuracy metric. Thus, it indeed captures a different perspective of quality. Lastly, based on this metric, we evaluate several existing methods for video detection and show how they affect accuracy and stability. We believe our work can provide guidance and solid baselines for future researches in the related areas.

Connected through hyperedge HE-1 (induced by source paper "rethinking temporal object detection from robotic perspectives"):
  with members:
    - "joint anchor feature refinement for real time accurate object detection in images and videos"
    - "siamfc towards robust and accurate visual tracking with target estimation guidelines"
    - "atom accurate tracking by overlap maximization"
    - "object detection in video with spatiotemporal sampling networks"
    - "temporally identity aware ssd with attentional lstm"
    - "towards high performance video object detection"
    - "focal loss for dense object detection"
    - "single shot refinement neural network for object detection"

Connected through hyperedge HE-2 (induced by source paper "analysis and a solution of momentarily missed detection for anchor based object detectors"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "metaanchor learning to detect objects with customized anchors"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "flow guided feature aggregation for video object detection"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "continuity stability and integration novel tracking based perspectives for temporal object detection"):
  with members:
    - "siamfc towards robust and accurate visual tracking with target estimation guidelines"
    - "atom accurate tracking by overlap maximization"
    - "object detection in video with spatiotemporal sampling networks"
    - "detect or track towards cost effective video object detection tracking"
    - "fast online object tracking and segmentation a unifying approach"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "towards high performance video object detection"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "integrated object detection and tracking with tracklet conditioned detection"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "cornernet detecting objects as paired keypoints"
    - "deformable convnets v2 more deformable better results"
    - "towards high performance video object detection"
    - "flow guided feature aggregation for video object detection"
    - "deformable convolutional networks"
    - "dual path networks"
    - "detect to track and track to detect"

Connected through hyperedge HE-5 (induced by source paper "pose flow efficient online pose tracking"):
  with members:
    - "detecting and recognizing human object interactions"
    - "posetrack a benchmark for human pose estimation and tracking"
    - "cascaded pyramid network for multi person pose estimation"
    - "pose invariant embedding for deep person re identification"
    - "detect and track efficient pose estimation in videos"
    - "multi context attention for human pose estimation"
    - "stacked hourglass networks for human pose estimation"
    - "posetrack joint multi person pose estimation and tracking"

Connected through hyperedge HE-6 (induced by source paper "flow guided feature aggregation for video object detection"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "deformable convolutional networks"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"
    - "stfcn spatio temporal fcn for semantic video segmentation"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "object detection from video tubelets with 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)