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 "an lstm network for real time odometry estimation"):
  Abstract: The use of 2D laser scanners is attractive for the autonomous driving industry because of its accuracy, light-weight and low-cost. However, since only a 2D slice of the surrounding environment is detected at each scan, it is a challenge to execute important tasks such as the localization of the vehicle. In this paper we present a novel framework that explores the use of deep Recurrent Convolutional Neural Networks (RCNN) for odometry estimation using only 2D laser scanners. The application of RCNNs provides the tools to not only extract the features of the laser scanner data using Convolutional Neural Networks (CNNs), but in addition it models the possible connections among consecutive scans using the Long Short-Term Memory (LSTM) Recurrent Neural Network. Results on a real road dataset show that the method can run in real-time without using GPU acceleration and have competitive performance compared to other methods, being an interesting approach that could complement traditional localization systems.

Includes members:
  Member "an lstm network for highway trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overview of tools supporting planning for automated driving")
  Member "end to end race driving with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles")
  Member "imls slam scan to model matching based on 3d data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description")
  Member "coupled longitudinal and lateral control of a vehicle using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep sensor fusion for real time odometry estimation")
  Member "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "undeepvo monocular visual odometry through unsupervised deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry")
  Member "3d fully convolutional network for vehicle detection in point cloud":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "posenet a convolutional network for real time 6 dof camera relocalization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")

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)