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: "offline object extraction from dynamic occupancy grid map sequences"
  Abstract: A dynamic occupancy grid map (DOGMa) allows a fast, robust, and complete environment representation for automated vehicles. Dynamic objects in a DOGMa, however, are commonly represented as independent cells while modeled objects with shape and pose are favorable. The evaluation of algorithms for object extraction or the training and validation of learning algorithms rely on labeled ground truth data. Manually annotating objects in a DOGMa to obtain ground truth data is a time consuming and expensive process. Additionally the quality of labeled data depend strongly on the variation of filtered input data. The presented work introduces an automatic labeling process, where a full sequence is used to extract the best possible object pose and shape in terms of temporal consistency. A two direction temporal search is executed to trace single objects over a sequence, where the best estimate of its extent and pose is refined in every time step. Furthermore, the presented algorithm only uses statistical constraints of the cell clusters for the object extraction instead of fixed heuristic parameters. Experimental results show a well-performing automatic labeling algorithm with real sensor data even at challenging scenarios.

Connected through hyperedge HE-1 (induced by source paper "dynamic environment prediction in urban scenes using recurrent representation learning"):
  with members:
    - "2d lidar map prediction via estimating motion flow with gru"
    - "deep object tracking on dynamic occupancy grid maps using rnns"
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "long term occupancy grid prediction using recurrent neural networks"
    - "multi step prediction of occupancy grid maps with recurrent neural networks"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "stochastic variational video prediction"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"

Connected through hyperedge HE-2 (induced by source paper "motion estimation in occupancy grid maps in stationary settings using recurrent neural networks"):
  with members:
    - "short term prediction and multi camera fusion on semantic grids"
    - "2d lidar map prediction via estimating motion flow with gru"
    - "self supervised flow estimation using geometric regularization with applications to camera image and grid map sequences"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "deep object tracking on dynamic occupancy grid maps using rnns"
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "long term occupancy grid prediction using recurrent neural networks"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"

Connected through hyperedge HE-3 (induced by source paper "long term occupancy grid prediction using recurrent neural networks"):
  with members:
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "end to end tracking and semantic segmentation using recurrent neural networks"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "a random finite set approach for dynamic occupancy grid maps with real time application"
    - "unsupervised learning of video representations using lstms"
    - "u net convolutional networks for biomedical image segmentation"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-4 (induced by source paper "deep object tracking on dynamic occupancy grid maps using rnns"):
  with members:
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "mask r cnn"
    - "fully convolutional neural networks for dynamic object detection in grid maps"
    - "a random finite set approach for dynamic occupancy grid maps with real time application"
    - "ssd single shot multibox detector"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"
    - "u net convolutional networks for biomedical image segmentation"

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)