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: "model free adaptive optimal control of sequential manufacturing processes using reinforcement learning"
  Abstract: A self-learning optimal control algorithm for sequential manufacturing processes with time-discrete control actions is proposed and evaluated with simulated deep drawing processes. The necessary control model is built during consecutive process executions under optimal control via Reinforcement Learning, using the measured product quality as reward after each process execution. Prior model formation, which is required by state-of-the-art algorithms like Model Predictive Control and Approximate Dynamic Programming, is therefore obsolete. This avoids the difficulties in system identification and accurate modelling, which arise with processes subject to non-linear dynamics and stochastic influences. Also runtime complexity problems of these approaches are avoided, which arise when more complex models and larger control prediction horizons are employed. Instead of using pre-created process- and observation-models, Reinforcement Learning algorithms build functions of expected future reward during processing, which are then used for optimal process control decisions. The learning of such expectation functions is realized online by interacting with the process. The proposed algorithm also takes stochastic variations of the process conditions into consideration and is able to cope with partial observability. A method for the adaptive optimal control of partially observable fixed-horizon manufacturing processes, based on Q-learning is developed and studied. The resulting algorithm is instantiated and then evaluated by application to a time-stochastic optimal control problem in metal sheet deep drawing, where the experiments use FEM-simulated processes. The Reinforcement Learning based control shows superior results over the model-based Model Predictive Control and Approximate Dynamic Programming approaches.

Connected through hyperedge HE-1 (induced by source paper "multiobjective reinforcement learning for reconfigurable adaptive optimal control of manufacturing processes"):
  with members:
    - "a survey of multi objective sequential decision making"

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)