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: "simultaneous localization and mapping without linearization"
  Abstract: We apply a combination of linear time varying (LTV) Kalman filtering and nonlinear contraction tools to the problem of simultaneous mapping and local- ization (SLAM), in a fashion which avoids linearized approximations altogether. By exploiting virtual synthetic measurements, the LTV Kalman observer avoids errors and approximations brought by the linearization process in the EKF SLAM. Fur- thermore, conditioned on the robot position, the covariances between landmarks are fully decoupled, making the algorithm easily scalable. Contraction analysis is used to establish stability of the algorithm and quantify its convergence rate. We pro- pose four versions based on different combinations of sensor information, ranging from traditional bearing measurements and radial measurements to optical flows and time-to-contact measurements. As shown in simulations, the proposed algorithm is simple and fast, and it can solve SLAM problems in both 2D and 3D scenarios with guaranteed convergence rates in a full nonlinear context.

Connected through hyperedge HE-1 (induced by source paper "numerical and experimental realization of analytical slam"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "estimating uncertain spatial relationships in robotics"

Connected through hyperedge HE-2 (induced by source paper "analytical slam without linearization"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "estimating uncertain spatial relationships in robotics"
    - "efficient touch based localization through submodularity"
    - "from angular manifolds to the integer lattice guaranteed orientation estimation with application to pose graph optimization"

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)