human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "motion estimation in occupancy grid maps in stationary settings using recurrent neural networks"):
  Abstract: In this work, we tackle the problem of modeling the vehicle environment as dynamic occupancy grid map in complex urban scenarios using recurrent neural networks. Dynamic occupancy grid maps represent the scene in a bird's eye view, where each grid cell contains the occupancy probability and the two dimensional velocity. As input data, our approach relies on measurement grid maps, which contain occupancy probabilities, generated with lidar measurements. Given this configuration, we propose a recurrent neural network architecture to predict a dynamic occupancy grid map, i.e. filtered occupancy and velocity of each cell, by using a sequence of measurement grid maps. Our network architecture contains convolutional long-short term memories in order to sequentially process the input, makes use of spatial context, and captures motion. In the evaluation, we quantify improvements in estimating the velocity of braking and turning vehicles compared to the state-of-the-art. Additionally, we demonstrate that our approach provides more consistent velocity estimates for dynamic objects, as well as, less erroneous velocity estimates in static area.

Includes members:
  Member "short term prediction and multi camera fusion on semantic grids":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "boosting real time driving scene parsing with shared semantics")
  Member "2d lidar map prediction via estimating motion flow with gru":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic environment prediction in urban scenes using recurrent representation learning")
  Member "self supervised flow estimation using geometric regularization with applications to camera image and grid map sequences":
    also connected through other hyperedges:
      (none)
  Member "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving")
  Member "deep object tracking on dynamic occupancy grid maps using rnns":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic environment prediction in urban scenes using recurrent representation learning")
  Member "offline object extraction from dynamic occupancy grid map sequences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic environment prediction in urban scenes using recurrent representation learning")
  Member "object detection on dynamic occupancy grid maps using deep learning and automatic label generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distant vehicle detection using radar and vision")
  Member "long term occupancy grid prediction using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps")

Question: Please tell me which class the source paper 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)