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: "contact aided invariant extended kalman filtering for legged robot state estimation"
  Abstract: This paper derives a contact-aided inertial navigation observer for a 3D bipedal robot using the theory of invariant observer design. Aided inertial navigation is fundamentally a nonlinear observer design problem; thus, current solutions are based on approximations of the system dynamics, such as an Extended Kalman Filter (EKF), which uses a system's Jacobian linearization along the current best estimate of its trajectory. On the basis of the theory of invariant observer design by Barrau and Bonnabel, and in particular, the Invariant EKF (InEKF), we show that the error dynamics of the point contact-inertial system follows a log-linear autonomous differential equation; hence, the observable state variables can be rendered convergent with a domain of attraction that is independent of the system's trajectory. Due to the log-linear form of the error dynamics, it is not necessary to perform a nonlinear observability analysis to show that when using an Inertial Measurement Unit (IMU) and contact sensors, the absolute position of the robot and a rotation about the gravity vector (yaw) are unobservable. We further augment the state of the developed InEKF with IMU biases, as the online estimation of these parameters has a crucial impact on system performance. We evaluate the convergence of the proposed system with the commonly used quaternion-based EKF observer using a Monte-Carlo simulation. In addition, our experimental evaluation using a Cassie-series bipedal robot shows that the contact-aided InEKF provides better performance in comparison with the quaternion-based EKF as a result of exploiting symmetries present in the system dynamics.

Connected through hyperedge HE-1 (induced by source paper "a code for unscented kalman filtering on manifolds ukf m"):
  with members:
    - "exploiting symmetries to design ekfs with consistency properties for navigation and slam"
    - "a micro lie theory for state estimation in robotics"
    - "an ekf slam algorithm with consistency properties"
    - "quaternion kinematics for the error state kalman filter"
    - "the invariant extended kalman filter as a stable observer"
    - "intrinsic filtering on lie groups with applications to attitude estimation"
    - "integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds"

Connected through hyperedge HE-2 (induced by source paper "rins w robust inertial navigation system on wheels"):
  with members:
    - "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"
    - "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-3 (induced by source paper "bayesian spatial kernel smoothing for scalabledense semantic mapping"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring"
    - "rtseg real time semantic segmentation comparative study"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "gaussian processes semantic map representation"
    - "sparse bayesian inference for dense semantic mapping"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "probe gk predictive robust estimation using generalized kernels"

Connected through hyperedge HE-4 (induced by source paper "lidartag a real time fiducial tag using point clouds"):
  with members:
    - "real time rotation invariant face detection with progressive calibration networks"
    - "rotation equivariant vector field networks"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "harmonic networks deep translation and rotation equivariance"
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"
    - "3d fully convolutional network for vehicle detection in point cloud"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"

Connected through hyperedge HE-5 (induced by source paper "adaptive continuous visual odometry from rgb d images"):
  with members:
    - "closed form preintegration methods for graph based visual inertial navigation"
    - "hybrid contact preintegration for visual inertial contact state estimation using factor graphs"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "on manifold preintegration for real time visual inertial odometry"
    - "direct sparse odometry"

Connected through hyperedge HE-6 (induced by source paper "a mathematical framework for imu error propagation with applications to preintegration"):
  with members:
    - "closed form preintegration methods for graph based visual inertial navigation"
    - "an invariant linear quadratic gaussian controller for a simplified car"
    - "the invariant extended kalman filter as a stable observer"
    - "intrinsic filtering on lie groups with applications to attitude estimation"
    - "integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds"

Connected through hyperedge HE-7 (induced by source paper "continuous direct sparse visual odometry from rgb d images"):
  with members:
    - "closed form preintegration methods for graph based visual inertial navigation"
    - "hybrid contact preintegration for visual inertial contact state estimation using factor graphs"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "on manifold preintegration for real time visual inertial odometry"
    - "direct sparse odometry"

Connected through hyperedge HE-8 (induced by source paper "dynamic walking with compliance on a cassie bipedal robot"):
  with members:
    - "feedback control of a cassie bipedal robot walking standing and riding a segway"

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)