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: "comparing metrics for robustness against external perturbations in dynamic trajectory optimization"
  Abstract: Dynamic trajectory optimization is a popular approach for generating optimal and dynamically consistent trajectories. In order to deal with model errors and perturbations, the trajectories are usually tracked with feedback controllers. Their robustness thus largely depends on the margins of stability and control authority the system retains. Manipulability ellipsoids and force polytopes are well-known tools for evaluating force and motion capabilities of robot manipulators. Increased control authority can be achieved by incorporating task constraints within those tools. However, they come with an increased computational cost. Additionally, their impact on resulting trajectory quality and control authority has not yet been benchmarked and compared. In this letter, we introduce a novel robustness metric, the residual force polytope, which takes the nominal torque required to maintain a posture into account. We further detail a benchmarking protocol including evaluation criteria and visualization tools to compare robustness metrics in dynamic trajectory optimization. To foster benchmarking and allow for reproducibility, we open source a flexible framework for dynamic trajectory optimization via direct transcription along with our benchmark protocols as supplementary materials. Finally, we include - to the best of our knowledge - the first holistic comparison between traditional energy minimization metrics, kinematic manipulability maximization, and force polytope methods.

Connected through hyperedge HE-1 (induced by source paper "optimizing dynamic trajectories for robustness to disturbances using polytopic projections"):
  with members:
    - "optimisation of body ground contact for augmenting whole body loco manipulation of quadruped robots"
    - "a differentiable augmented lagrangian method for bilevel nonlinear optimization"
    - "crocoddyl an efficient and versatile framework for multi contact optimal control"
    - "application of wrench based feasibility analysis to the online trajectory optimization of legged robots"
    - "a family of iterative gauss newton shooting methods for nonlinear optimal control"
    - "julia a fresh approach to numerical computing"
    - "on computing the shadows and slices of polytopes"

Connected through hyperedge HE-2 (induced by source paper "optimisation of body ground contact for augmenting whole body loco manipulation of quadruped robots"):
  with members:
    - "bounded haptic teleoperation of a quadruped robot s foot posture for sensing and manipulation"

Connected through hyperedge HE-3 (induced by source paper "bounded haptic teleoperation of a quadruped robot s foot posture for sensing and manipulation"):
  with members:
    - "application of wrench based feasibility analysis to the online trajectory optimization of legged robots"
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"

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)