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: "steap simultaneous trajectory estimation and planning"
  Abstract: We present a unified probabilistic framework for simultaneous trajectory estimation and planning. Estimation and planning problems are usually considered separately, however, within our framework we show that solving them simultaneously can be more accurate and efficient. The key idea is to compute the full continuous-time trajectory from start to goal at each time-step. While the robot traverses the trajectory, the history portion of the trajectory signifies the solution to the estimation problem, and the future portion of the trajectory signifies a solution to the planning problem. Building on recent probabilistic inference approaches to continuous-time localization and mapping and continuous-time motion planning, we solve the joint problem by iteratively recomputing the maximum a posteriori trajectory conditioned on all available sensor data and cost information. Our approach can contend with high-degree-of-freedom trajectory spaces, uncertainty due to limited sensing capabilities, model inaccuracy, the stochastic effect of executing actions, and can find a solution in real-time. We evaluate our framework empirically in both simulation and on a mobile manipulator.

Connected through hyperedge HE-1 (induced by source paper "spatial concept based navigation with human speech instructions via probabilistic inference on bayesian generative model"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "visual reinforcement learning with imagined goals"
    - "improved and scalable online learning of spatial concepts and language models with mapping"
    - "online spatial concept and lexical acquisition with simultaneous localization and mapping"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "spatial concept acquisition for a mobile robot that integrates self localization and unsupervised word discovery from spoken sentences"
    - "symbol emergence in robotics a survey"

Connected through hyperedge HE-2 (induced by source paper "robot calligraphy using pseudospectral optimal control in conjunction with a novel dynamic brush model"):
  with members:
    - "reducing the barrier to entry of complex robotic software a moveit case study"

Connected through hyperedge HE-3 (induced by source paper "a unified method for solving inverse forward and hybrid manipulator dynamics using factor graphs"):
  with members:
    - "hybrid contact preintegration for visual inertial contact state estimation using factor graphs"
    - "on manifold preintegration for real time visual inertial odometry"

Connected through hyperedge HE-4 (induced by source paper "continuous time gaussian process motion planning via probabilistic inference"):
  with members:
    - "approximately optimal continuous time motion planning and control via probabilistic inference"
    - "functional gradient motion planning in reproducing kernel hilbert spaces"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "batch nonlinear continuous time trajectory estimation as exactly sparse gaussian process regression"
    - "incremental sampling based algorithms for optimal motion planning"

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)