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 "robust data driven zero velocity detection for foot mounted inertial navigation"):
  Abstract: We present two novel techniques for detecting zero-velocity events to improve foot-mounted inertial navigation. Our first technique augments a classical zero-velocity detector by incorporating a motion classifier that adaptively updates the detector’s threshold parameter. Our second technique uses a long short-term memory (LSTM) recurrent neural network to classify zero-velocity events from raw inertial data, in contrast to the majority of zero-velocity detection methods that rely on basic statistical hypothesis testing. We demonstrate that both of our proposed detectors achieve higher accuracies than existing detectors for trajectories including walking, running, and stair-climbing motions. Additionally, we present a straightforward data augmentation method that is able to extend the LSTM-based model to different inertial sensors without the need to collect new training data.

Includes members:
  Member "mobile stride length estimation with deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lstm based zero velocity detection for robust inertial navigation")
  Member "ionet learning to cure the curse of drift in inertial odometry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference")
  Member "improving foot mounted inertial navigation through real time motion classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lstm based zero velocity detection for robust inertial navigation")
  Member "backprop kf learning discriminative deterministic state estimators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")
  Member "a critical review of recurrent neural networks for sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)