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: "a random finite set approach for dynamic occupancy grid maps with real time application"
  Abstract: Grid mapping is a well established approach for environment perception in robotic and automotive applications. Early work suggests estimating the occupancy state of each grid cell in a robot's environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter (BBF). A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a real-time application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.

Connected through hyperedge HE-1 (induced by source paper "learned enrichment of top view grid maps improves object detection"):
  with members:
    - "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "pointpillars fast encoders for object detection from point clouds"
    - "evidential occupancy grid map augmentation using deep learning"
    - "attention u net learning where to look for the pancreas"
    - "occupancy networks learning 3d reconstruction in function space"
    - "focal loss for dense object detection"
    - "free space estimation using occupancy grids and dynamic object detection"

Connected through hyperedge HE-2 (induced by source paper "deep learning based vehicle behaviour prediction for autonomous driving applications a review"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "grip graph based interaction aware trajectory prediction"
    - "human motion trajectory prediction a survey"
    - "interaction aware kalman neural networks for trajectory prediction"
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "anomaly detection in road traffic using visual surveillance a survey"
    - "survey on vision based path prediction"

Connected through hyperedge HE-3 (induced by source paper "analyzing the cross sensor portability of neural network architectures for lidar based semantic labeling"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "self supervised generalisation with meta auxiliary learning"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "pointpillars fast encoders for object detection from point clouds"
    - "boosting lidar based semantic labeling by cross modal training data generation"
    - "pointseg real time semantic segmentation based on 3d lidar point cloud"
    - "birdnet a 3d object detection framework from lidar information"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "deep grid net dgn a deep learning system for real time driving context understanding"):
  with members:
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "population based training of neural networks"
    - "end to end tracking and semantic segmentation using recurrent neural networks"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "fusion of object tracking and dynamic occupancy grid map"):
  with members:
    - "environment perception framework fusing multi object tracking dynamic occupancy grid maps and digital maps"
    - "extended object tracking introduction overview and applications"

Connected through hyperedge HE-8 (induced by source paper "long term occupancy grid prediction using recurrent neural networks"):
  with members:
    - "offline object extraction from dynamic occupancy grid map sequences"
    - "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"
    - "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"

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.RO(Robotics)