human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "adaptive mpc for iterative tasks"):
  Abstract: This paper proposes an Adaptive Learning Model Predictive Control strategy for uncertain constrained linear systems performing iterative tasks. The additive uncertainty is modeled as the sum of a bounded process noise and an unknown constant offset. As new data becomes available, the proposed algorithm iteratively adapts the believed domain of the unknown offset after each iteration. An MPC strategy robust to all feasible offsets is employed in order to guarantee recursive feasibility. We show that the adaptation of the feasible offset domain reduces conservatism of the proposed strategy, compared to classical robust MPC strategies. As a result, the controller performance improves. Performance is measured in terms of following trajectories with lower associated costs at each iteration. Numerical simulations highlight the main advantages of the proposed approach.

Includes members:
  Member "adaptive mpc with chance constraints for fir systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive trajectory planning and optimization at limits of handling")
  Member "adaptive mpc for autonomous lane keeping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive trajectory planning and optimization at limits of handling")
  Member "cautious model predictive control using gaussian process regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "practical reinforcement learning for mpc learning from sparse objectives in under an hour on a real robot")
  Member "a scenario approach for non convex control design":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimization under uncertainty in the era of big data and deep learning when machine learning meets mathematical programming")
  Member "learning model predictive control for iterative tasks a data driven control framework":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "information theoretic model predictive q learning")

Question: Please tell me which class the source paper 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)