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: "followme efficient online min cost flow tracking with bounded memory and computation"
  Abstract: One of the most popular approaches to multi-target tracking is tracking-by-detection. Current min-cost flow algorithms which solve the data association problem optimally have three main drawbacks: they are computationally expensive, they assume that the whole video is given as a batch, and they scale badly in memory and computation with the length of the video sequence. In this paper, we address each of these issues, resulting in a computationally and memory-bounded solution. First, we introduce a dynamic version of the successive shortest-path algorithm which solves the data association problem optimally while reusing computation, resulting in significantly faster inference than standard solvers. Second, we address the optimal solution to the data association problem when dealing with an incoming stream of data (i.e., online setting). Finally, we present our main contribution which is an approximate online solution with bounded memory and computation which is capable of handling videos of arbitrarily length while performing tracking in real time. We demonstrate the effectiveness of our algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art performance, while being significantly faster than existing solvers.

Connected through hyperedge HE-1 (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-2 (induced by source paper "sdvtracker real time multi sensor association and tracking for self driving vehicles"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "robust multi modality multi object tracking"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "uncertainty aware short term motion prediction of traffic actors for autonomous driving"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "online visual robot tracking and identification using deep lstm networks"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "robust multi modality multi object tracking"):
  with members:
    - "deep multi sensor lane detection"
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "end to end learning of multi sensor 3d tracking by detection"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "combined image and world space tracking in traffic scenes"
    - "the perfect match 3d point cloud matching with smoothed densities"

Connected through hyperedge HE-4 (induced by source paper "complexer yolo real time 3d object detection and tracking on semantic point clouds"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "yolov3 an incremental improvement"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"

Connected through hyperedge HE-5 (induced by source paper "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "online multi object tracking with dual matching attention networks"
    - "learning non uniform hypergraph for multi object tracking"
    - "combined image and world space tracking in traffic scenes"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "learning optimal parameters for multi target tracking with contextual interactions"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "alignnet 3d fast point cloud registration of partially observed objects"):
  with members:
    - "mots multi object tracking and segmentation"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "open3d a modern library for 3d data processing"
    - "combined image and world space tracking in traffic scenes"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "dynamic graph cnn for learning on point clouds"
    - "the 2018 davis challenge on video object segmentation"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-7 (induced by source paper "live reconstruction of large scale dynamic outdoor worlds"):
  with members:
    - "pointflownet learning representations for rigid motion estimation from point clouds"
    - "fusion volumetric object level slam"
    - "open3d a modern library for 3d data processing"
    - "yolov3 an incremental improvement"
    - "robust dense mapping for large scale dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "pyramid stereo matching network"
    - "mid fusion octree based object level multi instance dynamic slam"

Connected through hyperedge HE-8 (induced by source paper "efficient global multi object tracking under minimum cost circulation framework"):
  with members:
    - "end to end learning deep crf models for multi object tracking"
    - "a baseline for 3d multi object tracking"
    - "online multiple pedestrian tracking using deep temporal appearance matching association"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "machine learning methods for solving assignment problems in multi target tracking"
    - "fusion of head and full body detectors for multi object tracking"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"

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)