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: "data driven robust control for type 1 diabetes under meal and exercise uncertainties"
  Abstract: We present a fully closed-loop design for an artificial pancreas (AP) which regulates the delivery of insulin for the control of Type I diabetes. Our AP controller operates in a fully automated fashion, without requiring any manual interaction (e.g. in the form of meal announcements) with the patient. A major obstacle to achieving closed-loop insulin control is the uncertainty in those aspects of a patient's daily behavior that significantly affect blood glucose, especially in relation to meals and physical activity. To handle such uncertainties, we develop a data-driven robust model-predictive control framework, where we capture a wide range of individual meal and exercise patterns using uncertainty sets learned from historical data. These sets are then used in the controller and state estimator to achieve automated, precise, and personalized insulin therapy. We provide an extensive in silico evaluation of our robust AP design, demonstrating the potential of this approach, without explicit meal announcements, to support high carbohydrate disturbances and to regulate glucose levels in large clusters of virtual patients learned from population-wide survey data.

Connected through hyperedge HE-1 (induced by source paper "mpc guided imitation learning of neural network policies for the artificial pancreas"):
  with members:
    - "pac confidence sets for deep neural networks via calibrated prediction"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "safe end to end imitation learning for model predictive control"
    - "representation and reinforcement learning for personalized glycemic control in septic patients"
    - "dropoutdagger a bayesian approach to safe imitation learning"
    - "generative adversarial imitation learning"
    - "plato policy learning using adaptive trajectory optimization"

Connected through hyperedge HE-2 (induced by source paper "neural simplex architecture"):
  with members:
    - "safety aware adaptive reinforcement learning with applications to brushbot navigation"
    - "reward constrained policy optimization"
    - "a lyapunov based approach to safe reinforcement learning"
    - "verification for machine learning autonomy and neural networks survey"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "constrained policy optimization"
    - "safe reinforcement learning via shielding"

Connected through hyperedge HE-3 (induced by source paper "a logic based learning approach to explore diabetes patient behaviors"):
  with members:
    - "behavior recognition and analysis in smart environments for context aware applications"
    - "robust online monitoring of signal temporal logic"

Connected through hyperedge HE-4 (induced by source paper "automated synthesis of safe and robust pid controllers for stochastic hybrid systems"):
  with members:
    - "invariant checking of nra transition systems via incremental reduction to lra with euf"
    - "delta decidability over the reals"

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)