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 "deep learning based pedestrian inertial navigation methods dataset and on device inference"):
  Abstract: Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. Recently, there has been a growing interest in applying deep neural networks (DNNs) to motion sensing and location estimation. However, the lack of sufficient labelled data for training and evaluating architecture benchmarks has limited the adoption of DNNs in IMU-based tasks. In this paper, we present and release the Oxford Inertial Odometry Dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research, with fine-grained ground-truth on all sequences. Furthermore, to enable more efficient inference at the edge, we propose a novel lightweight framework to learn and reconstruct pedestrian trajectories from raw IMU data. Extensive experiments show the effectiveness of our dataset and methods in achieving accurate data-driven pedestrian inertial navigation on resource-constrained devices.

Includes members:
  Member "advio an authentic dataset for visual inertial odometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "brno urban dataset the new data for self driving agents and mapping tasks")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")
  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 "robust data driven zero velocity detection for foot mounted inertial navigation")
  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 "robust data driven zero velocity detection for foot mounted inertial navigation")
  Member "the tum vi benchmark for evaluating visual inertial odometry":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")

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)