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 "rins w robust inertial navigation system on wheels"):
  Abstract: This paper proposes a real-time approach for long-term inertial navigation based only on an Inertial Measurement Unit (IMU) for self-localizing wheeled robots. The approach builds upon two components: 1) a robust detector that uses recurrent deep neural networks to dynamically detect a variety of situations of interest, such as zero velocity or no lateral slip; and 2) a state-of-the-art Kalman filter which incorporates this knowledge as pseudo-measurements for localization. Evaluations on a publicly available car dataset demonstrates that the proposed scheme may achieve a final precision of 20 m for a 21 km long trajectory of a vehicle driving for over an hour, equipped with an IMU of moderate precision (the gyro drift rate is 10 deg/h). To our knowledge, this is the first paper which combines sophisticated deep learning techniques with state-of-the-art filtering methods for pure inertial navigation on wheeled vehicles and as such opens up for novel data-driven inertial navigation techniques. Moreover, albeit taylored for IMU-only based localization, our method may be used as a component for self-localization of wheeled robots equipped with a more complete sensor suite.

Includes members:
  Member "contact aided invariant extended kalman filtering for legged robot state estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a code for unscented kalman filtering on manifolds ukf m")
  Member "imls slam scan to model matching based on 3d data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description")
  Member "using inertial sensors for position and orientation estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "denoising imu gyroscopes with deep learning for open loop attitude estimation")
  Member "ionet learning to cure the curse of drift in inertial odometry":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "denoising imu gyroscopes with deep learning for open loop attitude estimation")
  Member "lstm based zero velocity detection for robust inertial navigation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference")
  Member "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference")
  Member "complex urban lidar data set":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes")
  Member "oxiod the dataset for deep inertial odometry":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "localization for ground robots on manifold representation integration re parameterization and optimization")

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)