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: "simple online and realtime tracking"
  Abstract: This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, detection quality is identified as a key factor influencing tracking performance, where changing the detector can improve tracking by up to 18.9%. Despite only using a rudimentary combination of familiar techniques such as the Kalman Filter and Hungarian algorithm for the tracking components, this approach achieves an accuracy comparable to state-of-the-art online trackers. Furthermore, due to the simplicity of our tracking method, the tracker updates at a rate of 260 Hz which is over 20x faster than other state-of-the-art trackers.

Connected through hyperedge HE-1 (induced by source paper "multi object tracking via end to end tracklet searching and ranking"):
  with members:
    - "deep learning in video multi object tracking a survey"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "on accurate evaluation of gans for language generation"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "tracklet association tracker an end to end learning based association approach for multi object tracking"
    - "evaluating text gans as language models"
    - "unsupervised person re identification by deep learning tracklet association"

Connected through hyperedge HE-2 (induced by source paper "pointtracknet an end to end network for 3 d object detection and tracking from point clouds"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "pointit a fast tracking framework based on 3d instance segmentation"
    - "autotrack a lightweight object detection and tracking system for the sae autodrive challenge"
    - "fantrack 3d multi object tracking with feature association network"
    - "end to end learning of multi sensor 3d tracking by detection"
    - "flownet3d learning scene flow in 3d point clouds"
    - "birdnet a 3d object detection framework from lidar information"
    - "focal loss in 3d object detection"

Connected through hyperedge HE-3 (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-4 (induced by source paper "towards interpretable and robust hand detection via pixel wise prediction"):
  with members:
    - "scale invariant fully convolutional network detecting hands efficiently"
    - "east an efficient and accurate scene text detector"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "simple online and realtime tracking with a deep association metric"
    - "brain tumor segmentation based on refined fully convolutional neural networks with a hierarchical dice loss"
    - "ssd single shot multibox detector"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "refinements in motion and appearance for online multi object tracking"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "tracking without bells and whistles"
    - "learning a neural solver for multiple object tracking"
    - "resolution invariant person re identification"
    - "online multi object tracking with dual matching attention networks"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "tracklet association tracker an end to end learning based association approach for multi object tracking"

Connected through hyperedge HE-6 (induced by source paper "arbee towards automated recognition of bodily expression of emotion in the wild"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "posetrack joint multi person pose estimation and tracking"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "cross view tracking for multi human 3d pose estimation at over 100 fps"):
  with members:
    - "cross view fusion for 3d human pose estimation"
    - "learnable triangulation of human pose"
    - "fast and robust multi person 3d pose estimation from multiple views"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "simple baselines for human pose estimation and tracking"
    - "exploiting temporal information for 3d human pose estimation"
    - "single shot multi person 3d pose estimation from monocular rgb"

Connected through hyperedge HE-8 (induced by source paper "towards automated swimming analytics using deep neural networks"):
  with members:
    - "yolov3 an incremental improvement"
    - "mv yolo motion vector aided tracking by semantic object detection"
    - "continuous video to simple signals for swimming stroke detection 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)