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: "track then decide category agnostic vision based multi object tracking"
  Abstract: The most common paradigm for vision-based multi-object tracking is tracking-by-detection, due to the availability of reliable detectors for several important object categories such as cars and pedestrians. However, future mobile systems will need a capability to cope with rich human-made environments, in which obtaining detectors for every possible object category would be infeasible. In this paper, we propose a model-free multi-object tracking approach that uses a category-agnostic image segmentation method to track objects. We present an efficient segmentation mask-based tracker which associates pixel-precise masks reported by the segmentation. Our approach can utilize semantic information whenever it is available for classifying objects at the track level, while retaining the capability to track generic unknown objects in the absence of such information. We demonstrate experimentally that our approach achieves performance comparable to state-of-the-art tracking-by-detection methods for popular object categories such as cars and pedestrians. Additionally, we show that the proposed method can discover and robustly track a large variety of other objects.

Connected through hyperedge HE-1 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "key instance selection for unsupervised video object segmentation"
    - "large scale object mining for object discovery from unlabeled video"
    - "an efficient 3d cnn for action object segmentation in video"
    - "video instance segmentation"
    - "video object segmentation using space time memory networks"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "dmm net differentiable mask matching network for video object segmentation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "aerial multi object tracking by detection using deep association networks"):
  with members:
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "online multi object tracking with dual matching attention networks"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "cornernet detecting objects as paired keypoints"
    - "deep cosine metric learning for person re identification"
    - "event based moving object detection and tracking"

Connected through hyperedge HE-4 (induced by source paper "large scale object mining for object discovery from unlabeled video"):
  with members:
    - "unsupervised hard example mining from videos for improved object detection"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "in defense of the triplet loss for person re identification"
    - "object category learning and retrieval with weak supervision"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (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-6 (induced by source paper "track to reconstruct and reconstruct to track"):
  with members:
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "mots multi object tracking and segmentation"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "combined image and world space tracking in traffic scenes"
    - "mono camera 3d multi object tracking using deep learning detections and pmbm filtering"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "robust dense mapping for large scale dynamic environments"
    - "premvos proposal generation refinement and merging for video object segmentation"

Connected through hyperedge HE-7 (induced by source paper "learning to optimally segment point clouds"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "pointpillars fast encoders for object detection from point clouds"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "joint 3d proposal generation and object detection from view aggregation"

Connected through hyperedge HE-8 (induced by source paper "mining minimal map segments for visual place classifiers"):
  with members:
    - "large scale object mining for object discovery from unlabeled video"
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "yolov3 an incremental improvement"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "soft nms improving object detection with one line of code"
    - "a method to segment maps from different modalities using free space layout maoris map of ripples segmentation"
    - "x view graph based semantic multi view localization"
    - "you only look once unified real time object detection"

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)