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: "using inertial sensors for position and orientation estimation"
  Abstract: In recent years, micro-machined electromechanical system (MEMS) inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial se ...

Connected through hyperedge HE-1 (induced by source paper "denoising imu gyroscopes with deep learning for open loop attitude estimation"):
  with members:
    - "visual inertial odometry of aerial robots"
    - "ronin robust neural inertial navigation in the wild benchmark evaluations and new methods"
    - "rins w robust inertial navigation system on wheels"
    - "ai imu dead reckoning"
    - "selfvio self supervised deep monocular visual inertial odometry and depth estimation"
    - "a mathematical framework for imu error propagation with applications to preintegration"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"

Connected through hyperedge HE-2 (induced by source paper "a review of computational approaches for evaluation of rehabilitation exercises"):
  with members:
    - "a deep learning framework for assessing physical rehabilitation exercises"
    - "parkinson s disease assessment from a wrist worn wearable sensor in free living conditions deep ensemble learning and visualization"
    - "time series segmentation through automatic feature learning"
    - "shoulder physiotherapy exercise recognition machine learning the inertial signals from a smartwatch"
    - "human motion analysis with deep metric learning"
    - "data augmentation of wearable sensor data for parkinson s disease monitoring using convolutional neural networks"
    - "exercise motion classification from large scale wearable sensor data using convolutional neural networks"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"

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"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "lstm based zero velocity detection for robust inertial navigation"
    - "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 "the sixth sense with artificial intelligence an innovative solution for real time retrieval of the human figure behind visual obstruction"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "infovae information maximizing variational autoencoders"
    - "on loss functions for deep neural networks in classification"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "estimating lower limb kinematics using a reduced wearable sensor count"):
  with members:
    - "deep inertial poser learning to reconstruct human pose from sparse inertial measurements in real time"

Connected through hyperedge HE-6 (induced by source paper "improving temporal interpolation of head and body pose using gaussian process regression in a matrix completion setting"):
  with members:
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "tracking gaze and visual focus of attention of people involved in social interaction"
    - "convolutional pose machines"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "salsa a novel dataset for multimodal group behavior analysis"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "deeppose human pose estimation via deep neural networks"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-7 (induced by source paper "scalable magnetic field slam in 3d using gaussian process maps"):
  with members:
    - "modeling and interpolation of the ambient magnetic field by gaussian processes"

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)