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 "mpc guided imitation learning of neural network policies for the artificial pancreas"):
  Abstract: Even though model predictive control (MPC) is currently the main algorithm for insulin control in the artificial pancreas (AP), it usually requires complex online optimizations, which are infeasible for resource-constrained medical devices. MPC also typically relies on state estimation, an error-prone process. In this paper, we introduce a novel approach to AP control that uses Imitation Learning to synthesize neural-network insulin policies from MPC-computed demonstrations. Such policies are computationally efficient and, by instrumenting MPC at training time with full state information, they can directly map measurements into optimal therapy decisions, thus bypassing state estimation. We apply Bayesian inference via Monte Carlo Dropout to learn policies, which allows us to quantify prediction uncertainty and thereby derive safer therapy decisions. We show that our control policies trained under a specific patient model readily generalize (in terms of model parameters and disturbance distributions) to patient cohorts, consistently outperforming traditional MPC with state estimation.

Includes members:
  Member "pac confidence sets for deep neural networks via calibrated prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "calibrated prediction with covariate shift via unsupervised domain adaptation")
  Member "deep k nearest neighbors towards confident interpretable and robust deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "plan online learn offline efficient learning and exploration via model based control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural lyapunov model predictive control")
  Member "safe end to end imitation learning for model predictive control":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "carpal confidence aware intent recognition for parallel autonomy")
  Member "representation and reinforcement learning for personalized glycemic control in septic patients":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforcement learning in healthcare a survey")
  Member "dropoutdagger a bayesian approach to safe imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "radgrad active learning with loss gradients")
  Member "data driven robust control for type 1 diabetes under meal and exercise uncertainties":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural simplex architecture")
  Member "generative adversarial imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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.LG(Machine Learning)