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: "hybrid contact preintegration for visual inertial contact state estimation using factor graphs"
  Abstract: The factor graph framework is a convenient modeling technique for robotic state estimation where states are represented as nodes, and measurements are modeled as factors. When designing a sensor fusion framework for legged robots, one often has access to visual, inertial, joint encoder, and contact sensors. While visual-inertial odometry has been studied extensively in this framework, the addition of a preintegrated contact factor for legged robots has been only recently proposed. This allowed for integration of encoder and contact measurements into existing factor graphs, however, new nodes had to be added to the graph every time contact was made or broken. In this work, to cope with the problem of switching contact frames, we propose a hybrid contact preintegration theory that allows contact information to be integrated through an arbitrary number of contact switches. The proposed hybrid modeling approach reduces the number of required variables in the nonlinear optimization problem by only requiring new states to be added alongside camera or selected keyframes. This method is evaluated using real experimental data collected from a Cassie-series robot where the trajectory of the robot produced by a motion capture system is used as a proxy for ground truth. The evaluation shows that inclusion of the proposed preintegrated hybrid contact factor alongside visual-inertial navigation systems improves estimation accuracy as well as robustness to vision failure, while its generalization makes it more accessible for legged platforms.

Connected through hyperedge HE-1 (induced by source paper "adaptive continuous visual odometry from rgb d images"):
  with members:
    - "closed form preintegration methods for graph based visual inertial navigation"
    - "contact aided invariant extended kalman filtering for legged robot state estimation"
    - "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-2 (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"
    - "contact aided invariant extended kalman filtering for legged robot state estimation"
    - "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-3 (induced by source paper "robust legged robot state estimation using factor graph optimization"):
  with members:
    - "fast and continuous foothold adaptation for dynamic locomotion through cnns"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "on manifold preintegration for real time visual inertial odometry"

Connected through hyperedge HE-4 (induced by source paper "a unified method for solving inverse forward and hybrid manipulator dynamics using factor graphs"):
  with members:
    - "steap simultaneous trajectory estimation and planning"
    - "on manifold preintegration for real time visual inertial odometry"

Connected through hyperedge HE-5 (induced by source paper "preintegrated velocity bias estimation to overcome contact nonlinearities in legged robot odometry"):
  with members:
    - "robust legged robot state estimation using factor graph optimization"
    - "on manifold preintegration for real time visual inertial odometry"

Connected through hyperedge HE-6 (induced by source paper "contact aided invariant extended kalman filtering for legged robot state estimation"):
  with members:
    - "rins w robust inertial navigation system on wheels"
    - "closed form preintegration methods for graph based visual inertial navigation"
    - "robocentric visual inertial odometry"
    - "feedback control of a cassie bipedal robot walking standing and riding a segway"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "invariant smoothing on lie groups"
    - "on manifold preintegration for real time visual inertial odometry"
    - "an ekf slam algorithm with consistency properties"

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)