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: "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
  Abstract: We tackle the problem of object detection and pose estimation in a shared space downtown environment. For perception multiple laser scanners with 360{\deg} coverage were fused in a dynamic occupancy grid map (DOGMa). A single-stage deep convolutional neural network is trained to provide object hypotheses comprising of shape, position, orientation and an existence score from a single input DOGMa. Furthermore, an algorithm for offline object extraction was developed to automatically label several hours of training data. The algorithm is based on a two-pass trajectory extraction, forward and backward in time. Typical for engineered algorithms, the automatic label generation suffers from misdetections, which makes hard negative mining impractical. Therefore, we propose a loss function counteracting the high imbalance between mostly static background and extremely rare dynamic grid cells. Experiments indicate, that the trained network has good generalization capabilities since it detects objects occasionally lost by the label algorithm. Evaluation reaches an average precision (AP) of 75.9%

Connected through hyperedge HE-1 (induced by source paper "distant vehicle detection using radar and vision"):
  with members:
    - "deep learning is robust to massive label noise"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "soft sampling for robust object detection"
    - "faster bounding box annotation for object detection in indoor scenes"
    - "detecting traffic lights by single shot detection"
    - "focal loss for dense object detection"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "dsod learning deeply supervised object detectors from scratch"

Connected through hyperedge HE-2 (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"
    - "offline object extraction from dynamic occupancy grid map sequences"
    - "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-3 (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"
    - "offline object extraction from dynamic occupancy grid map sequences"
    - "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-4 (induced by source paper "deep object tracking on dynamic occupancy grid maps using rnns"):
  with members:
    - "offline object extraction from dynamic occupancy grid map sequences"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "object detection and classification in occupancy grid maps using deep convolutional networks"):
  with members:
    - "evidential occupancy grid map augmentation using deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "r2cnn rotational region cnn for orientation robust scene text detection"
    - "ssd single shot multibox detector"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "offline object extraction from dynamic occupancy grid map sequences"):
  with members:
    - "fully convolutional neural networks for dynamic object detection in grid maps"
    - "extended object tracking introduction overview and applications"

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)