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: "fully convolutional neural networks for dynamic object detection in grid maps"
  Abstract: Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical applications. In this work, we present a methods that uses a deep convolutional neural network (CNN) to infer whether grid cells are covering a moving object or not. Compared to tracking approaches, that use e.g. a particle filter to estimate grid cell velocities and then make a decision for individual grid cells based on this estimate, our approach uses the entire grid map as input image for a CNN that inspects a larger area around each cell and thus takes the structural appearance in the grid map into account to make a decision. Compared to our reference method, our concept yields a performance increase from 83.9% to 97.2%. A runtime optimized version of our approach yields similar improvements with an execution time of just 10 milliseconds.

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"
    - "offline object extraction from dynamic occupancy grid map sequences"
    - "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"

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"
    - "offline object extraction from dynamic occupancy grid map sequences"
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "long term occupancy grid prediction using recurrent neural networks"

Connected through hyperedge HE-3 (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"
    - "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"
    - "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-4 (induced by source paper "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"):
  with members:
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "focal loss for dense object detection"
    - "mask r cnn"
    - "a random finite set approach for dynamic occupancy grid maps with real time application"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "measurement wise occlusion in multi object tracking"):
  with members:
    - "poisson multi bernoulli mixture filter direct derivation and implementation"
    - "generalized optimal sub pattern assignment metric"
    - "mot16 a benchmark for multi object tracking"
    - "a random finite set approach for dynamic occupancy grid maps with real time application"
    - "approximate evaluation of marginal association probabilities with belief propagation"
    - "hybrid poisson and multi bernoulli filters"

Connected through hyperedge HE-6 (induced by source paper "offline object extraction from dynamic occupancy grid map sequences"):
  with members:
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "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)