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: "lstm based zero velocity detection for robust inertial navigation"
  Abstract: We present a method to improve the accuracy of a zero-velocity-aided inertial navigation system (INS) by replacing the standard zero-velocity detector with a long short-term memory (LSTM) neural network. While existing threshold-based zero-velocity detectors are not robust to varying motion types, our learned model accurately detects stationary periods of the inertial measurement unit (IMU) despite changes in the motion of the user. Upon detection, zero-velocity pseudo-measurements are fused with a dead reckoning motion model in an extended Kalman filter (EKF). We demonstrate that our LSTM-based zero-velocity detector, used within a zero-velocity-aided INS, improves zero-velocity detection during human localization tasks. Consequently, localization accuracy is also improved. Our system is evaluated on more than 7.5 km of indoor pedestrian locomotion data, acquired from five different subjects. We show that 3D positioning error is reduced by over 34% compared to existing fixed-threshold zero-velocity detectors for walking, running, and stair climbing motions. Additionally, we demonstrate how our learned zero-velocity detector operates effectively during crawling and ladder climbing. Our system is calibration-free (no careful threshold-tuning is required) and operates consistently with differing users, IMU placements, and shoe types, while being compatible with any generic zero-velocity-aided INS.

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"
    - "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"
    - "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"
    - "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"
    - "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"
    - "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"
    - "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 "air writing translater a novel unsupervised domain adaptation method for inertia trajectory translation of in air handwriting"):
  with members:
    - "transferring physical motion between domains for neural inertial tracking"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "universal style transfer via feature transforms"
    - "perceptual losses for real time style transfer and super resolution"
    - "unsupervised cross domain image generation"
    - "combining markov random fields and convolutional neural networks for image synthesis"

Connected through hyperedge HE-5 (induced by source paper "contact inertial odometry collisions are your friend"):
  with members:
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "orb slam a versatile and accurate monocular slam system"
    - "the invariant extended kalman filter as a stable observer"
    - "improved state estimation in quadrotor mavs a novel drift free velocity estimator"

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.RO(Robotics)