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: "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"
  Abstract: Strapdown inertial navigation systems are sensitive to the quality of the data provided by the accelerometer and gyroscope. Low-grade IMUs in handheld smart-devices pose a problem for inertial odometry on these devices. We propose a scheme for constraining the inertial odometry problem by complementing non-linear state estimation by a CNN-based deep-learning model for inferring the momentary speed based on a window of IMU samples. We show the feasibility of the model using a wide range of data from an iPhone, and present proof-of-concept results for how the model can be combined with an inertial navigation system for three-dimensional inertial navigation.

Connected through hyperedge HE-1 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference"):
  with members:
    - "advio an authentic dataset for visual inertial odometry"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "lstm based zero velocity detection for robust inertial navigation"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "lstm a search space odyssey"
    - "ridi robust imu double integration"

Connected through hyperedge HE-2 (induced by source paper "robust data driven zero velocity detection for foot mounted inertial navigation"):
  with members:
    - "mobile stride length estimation with deep convolutional neural networks"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "lstm based zero velocity detection for robust inertial navigation"
    - "improving foot mounted inertial navigation through real time motion classification"
    - "backprop kf learning discriminative deterministic state estimators"
    - "a critical review of recurrent neural networks for sequence learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "rins w robust inertial navigation system on wheels"):
  with members:
    - "contact aided invariant extended kalman filtering for legged robot state estimation"
    - "imls slam scan to model matching based on 3d data"
    - "using inertial sensors for position and orientation estimation"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "lstm based zero velocity detection for robust inertial navigation"
    - "complex urban lidar data set"
    - "oxiod the dataset for deep inertial odometry"
    - "ridi robust imu double integration"

Connected through hyperedge HE-4 (induced by source paper "iterative path reconstruction for large scale inertial navigation on smartphones"):
  with members:
    - "advio an authentic dataset for visual inertial odometry"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "ridi robust imu double integration"
    - "pivo probabilistic inertial visual odometry for occlusion robust navigation"
    - "inertial odometry on handheld smartphones"

Connected through hyperedge HE-5 (induced by source paper "oxiod the dataset for deep inertial odometry"):
  with members:
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "lstm a search space odyssey"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "on manifold preintegration for real time visual inertial odometry"
    - "cooperative relative positioning of mobile users by fusing imu inertial and uwb ranging information"
    - "adam a method for stochastic optimization"

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.CV(Computer Vision and Pattern Recognition)