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: "implementation of nonlinear model predictive path following control for an industrial robot"
  Abstract: Many robotic applications, such as milling, gluing, or high precision measurements, require the precise following of a predefined geometric path. We investigate the real-time feasible implementation of model predictive path-following control for an industrial robot. We consider constrained output path following with and without reference speed assignment. Finally, we present the results of an implementation of the proposed model predictive path-following controller on a KUKA LWR IV robot.

Connected through hyperedge HE-1 (induced by source paper "safe and fast tracking control on a robot manipulator robust mpc and neural network control"):
  with members:
    - "large scale model predictive control with neural networks and primal active sets"
    - "learning an approximate model predictive controller with guarantees"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "funnel libraries for real time robust feedback motion planning"

Connected through hyperedge HE-2 (induced by source paper "time optimal path tracking via reachability analysis"):
  with members:
    - "a new approach to time optimal path parameterization based on reachability analysis"
    - "nonlinear model predictive control for constrained output path following"
    - "a general fast and robust implementation of the time optimal path parameterization algorithm"

Connected through hyperedge HE-3 (induced by source paper "time optimal path tracking online scaling with guarantees"):
  with members:
    - "a new approach to time optimal path parameterization based on reachability analysis"
    - "nonlinear model predictive control for constrained output path following"
    - "a general fast and robust implementation of the time optimal path parameterization algorithm"

Connected through hyperedge HE-4 (induced by source paper "on model predictive path following and trajectory tracking for industrial robots"):
  with members:
    - "nonlinear model predictive control for constrained output path following"

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.SY(Systems and Control)