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: "trajectory synthesis for fisher information maximization"
  Abstract: Estimation of model parameters in a dynamic system can be significantly improved with the choice of experimental trajectory. For general nonlinear dynamic systems, finding globally “best” trajectories is typically not feasible; however, given an initial estimate of the model parameters and an initial trajectory, we present a continuous-time optimization method that produces a locally optimal trajectory for parameter estimation in the presence of measurement noise. The optimization algorithm is formulated to find system trajectories that improve a norm on the Fisher information matrix (FIM). A double-pendulum cart apparatus is used to numerically and experimentally validate this technique. In simulation, the optimized trajectory increases the minimum eigenvalue of the FIM by three orders of magnitude, compared with the initial trajectory. Experimental results show that this optimized trajectory translates to an order-of-magnitude improvement in the parameter estimate error in practice.

Connected through hyperedge HE-1 (induced by source paper "active learning of dynamics for data driven control using koopman operators"):
  with members:
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "learning deep neural network representations for koopman operators of nonlinear dynamical systems"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "dynamic task execution using active parameter identification with the baxter research robot"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "model based control using koopman operators"
    - "pulse based control using koopman operator under parametric uncertainty"
    - "sequential action control closed form optimal control for nonlinear and nonsmooth systems"

Connected through hyperedge HE-2 (induced by source paper "gaussians on riemannian manifolds for robot learning and adaptive control"):
  with members:
    - "rmpflow a computational graph for automatic motion policy generation"
    - "the vector heat method"
    - "geometry aware manipulability learning tracking and transfer"
    - "a micro lie theory for state estimation in robotics"
    - "on manifold preintegration for real time visual inertial odometry"
    - "identification of fully physical consistent inertial parameters using optimization on manifolds"

Connected through hyperedge HE-3 (induced by source paper "combining parameter identification and trajectory optimization real time planning for information gain"):
  with members:
    - "adaptive control of robot manipulators with uncertain kinematics and dynamics"
    - "funnel libraries for real time robust feedback motion planning"

Connected through hyperedge HE-4 (induced by source paper "sampling based motion planning for active multirotor system identification"):
  with members:
    - "observability aware trajectory optimization for self calibration with application to uavs"
    - "linear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehicles"
    - "benchmarking motion planning algorithms an extensible infrastructure for analysis and visualization"

Connected through hyperedge HE-5 (induced by source paper "dynamic task execution using active parameter identification with the baxter research robot"):
  with members:
    - "sequential action control closed form optimal control for nonlinear and nonsmooth systems"
    - "optimal parameter identification for discrete mechanical systems with application to flexible object manipulation"

Connected through hyperedge HE-6 (induced by source paper "ergodic exploration of distributed information"):
  with members:
    - "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"
    - "efficient informative sensing using multiple robots"

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)