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: "pathtrack fast trajectory annotation with path supervision"
  Abstract: Progress in Multiple Object Tracking (MOT) has been historically limited by the size of the available datasets. We present an efficient framework to annotate trajectories and use it to produce a MOT dataset of unprecedented size. In our novel path supervision the annotator loosely follows the object with the cursor while watching the video, providing a path annotation for each object in the sequence. Our approach is able to turn such weak annotations into dense box trajectories. Our experiments on existing datasets prove that our framework produces more accurate annotations than the state of the art, in a fraction of the time. We further validate our approach by crowdsourcing the PathTrack dataset, with more than 15,000 person trajectories in 720 sequences. Tracking approaches can benefit training on such large-scale datasets, as did object recognition. We prove this by re-training an off-the-shelf person matching network, originally trained on the MOT15 dataset, almost halving the misclassification rate. Additionally, training on our data consistently improves tracking results, both on our dataset and on MOT15. On the latter, we improve the top-performing tracker (NOMT) dropping the number of IDSwitches by 18% and fragments by 5%.

Connected through hyperedge HE-1 (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-2 (induced by source paper "pointly supervised action localization"):
  with members:
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "extreme clicking for efficient object annotation"
    - "localizing actions from video labels and pseudo annotations"
    - "action tubelet detector for spatio temporal action localization"
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "spatial aware object embeddings for zero shot localization and classification of actions"
    - "tube convolutional neural network t cnn for action detection in videos"

Connected through hyperedge HE-3 (induced by source paper "efficient object annotation via speaking and pointing"):
  with members:
    - "natural vocabulary emerges from free form annotations"
    - "scaling egocentric vision the epic kitchens dataset"
    - "where are the blobs counting by localization with point supervision"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "fast object class labelling via speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "object referring in visual scene with spoken language"

Connected through hyperedge HE-4 (induced by source paper "end to end learning of driving models with surround view cameras and route planners"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "failure prediction for autonomous driving"
    - "automated map reading image based localisation in 2 d maps using binary semantic descriptors"
    - "computer vision for autonomous vehicles problems datasets and state of the art"

Connected through hyperedge HE-5 (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-6 (induced by source paper "spatio temporal instance learning action tubes from class supervision"):
  with members:
    - "pointly supervised action localization"
    - "localizing actions from video labels and pseudo annotations"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "action tubelet detector for spatio temporal action localization"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "human action localization with sparse spatial supervision"
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "incremental tube construction for human action detection"

Connected through hyperedge HE-7 (induced by source paper "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"):
  with members:
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "deep network flow for multi object tracking"
    - "globally consistent multi people tracking using motion patterns"
    - "mot16 a benchmark for multi object tracking"
    - "performance measures and a data set for multi target multi camera tracking"
    - "wide residual networks"
    - "temporal dynamic appearance modeling for online multi person tracking"

Connected through hyperedge HE-8 (induced by source paper "fast object class labelling via speech"):
  with members:
    - "where are the blobs counting by localization with point supervision"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "training object class detectors with click supervision"
    - "spot on action localization from pointly supervised proposals"
    - "embracing error to enable rapid crowdsourcing"
    - "what s the point semantic segmentation with point supervision"
    - "imagenet large scale visual recognition challenge"

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)