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: "recursive estimation of orientation based on the bingham distribution"
  Abstract: Directional estimation is a common problem in many tracking applications. Traditional filters such as the Kalman filter perform poorly because they fail to take the periodic nature of the problem into account. We present a recursive filter for directional data based on the Bingham distribution in two dimensions. The proposed filter can be applied to circular filtering problems with 180 degree symmetry, i.e., rotations by 180 degrees cannot be distinguished. It is easily implemented using standard numerical techniques and suitable for real-time applications. The presented approach is extensible to quaternions, which allow tracking arbitrary three-dimensional orientations. We evaluate our filter in a challenging scenario and compare it to a traditional Kalman filtering approach.

Connected through hyperedge HE-1 (induced by source paper "explaining the ambiguity of object detection and 6d pose from visual data"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep model based 6d pose refinement in rgb"
    - "pose estimation for objects with rotational symmetry"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "roi 10d monocular lifting of 2d detection to 6d pose and metric shape"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "wavelet based reflection symmetry detection via textural and color histograms"

Connected through hyperedge HE-2 (induced by source paper "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"):
  with members:
    - "convex relaxations for pose graph optimization with outliers"
    - "local optimality and generalization guarantees for the langevin algorithm via empirical metastability"
    - "rotation averaging and strong duality"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "cad priors for accurate and flexible instance reconstruction"
    - "camera pose filtering with local regression geodesics on the riemannian manifold of dual quaternions"
    - "a certifiably correct algorithm for synchronization over the special euclidean group"

Connected through hyperedge HE-3 (induced by source paper "using inertial sensors for position and orientation estimation"):
  with members:
    - "on manifold preintegration for real time visual inertial odometry"
    - "magnetometer calibration using inertial sensors"
    - "towards self calibrating inertial body motion capture"
    - "a primer on the differential calculus of 3d orientations"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "unscented orientation estimation based on the bingham distribution"
    - "robust inference for state space models with skewed measurement noise"
    - "modeling and interpolation of the ambient magnetic field by gaussian processes"

Connected through hyperedge HE-4 (induced by source paper "on wrapping the kalman filter and estimating with the so 2 group"):
  with members:
    - "robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering"

Connected through hyperedge HE-5 (induced by source paper "recursive bayesian filtering in circular state spaces"):
  with members:
    - "extended object tracking with random hypersurface models"

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.SY(Systems and Control)