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: "dropoutdagger a bayesian approach to safe imitation learning"
  Abstract: While imitation learning is becoming common practice in robotics, this approach often suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these issues by continually aggregating training data from both the expert and novice policies, but does not consider the impact of safety. We present a probabilistic extension to DAgger, which uses the distribution over actions provided by the novice policy, for a given observation. Our method, which we call DropoutDAgger, uses dropout to train the novice as a Bayesian neural network that provides insight to its confidence. Using the distribution over the novice's actions, we estimate a probabilistic measure of safety with respect to the expert action, tuned to balance exploration and exploitation. The utility of this approach is evaluated on the MuJoCo HalfCheetah and in a simple driving experiment, demonstrating improved performance and safety compared to other DAgger variants and classic imitation learning.

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"
    - "data driven robust control for type 1 diabetes under meal and exercise uncertainties"
    - "generative adversarial imitation learning"
    - "plato policy learning using adaptive trajectory optimization"

Connected through hyperedge HE-2 (induced by source paper "radgrad active learning with loss gradients"):
  with members:
    - "efficient exploration through bayesian deep q networks"
    - "query efficient imitation learning for end to end autonomous driving"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-3 (induced by source paper "deep imitative models for flexible inference planning and control"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "human motion trajectory prediction a survey"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "differentiable mpc for end to end planning and control"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "rethinking self driving multi task knowledge for better generalization and accident explanation ability"

Connected through hyperedge HE-4 (induced by source paper "safe end to end imitation learning for model predictive control"):
  with members:
    - "mpc inspired neural network policies for sequential decision making"
    - "deep predictive models for collision risk assessment in autonomous driving"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "adaptive probabilistic trajectory optimization via efficient approximate inference"
    - "very deep convolutional networks for large scale image recognition"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

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.AI(Artificial Intelligence)