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: "safe end to end imitation learning for model predictive control"
  Abstract: We propose the use of Bayesian networks, which provide both a mean value and an uncertainty estimate as output, to enhance the safety of learned control policies under circumstances in which a test-time input differs significantly from the training set. Our algorithm combines reinforcement learning and end-to-end imitation learning to simultaneously learn a control policy as well as a threshold over the predictive uncertainty of the learned model, with no hand-tuning required. Corrective action, such as a return of control to the model predictive controller or human expert, is taken when the uncertainty threshold is exceeded. We validate our method on fully-observable and vision-based partially-observable systems using cart-pole and autonomous driving simulations using deep convolutional Bayesian neural networks. We demonstrate that our method is robust to uncertainty resulting from varying system dynamics as well as from partial state observability.

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"
    - "representation and reinforcement learning for personalized glycemic control in septic patients"
    - "dropoutdagger a bayesian approach to safe imitation learning"
    - "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 "carpal confidence aware intent recognition for parallel autonomy"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "the trajectron probabilistic multi agent trajectory modeling with dynamic spatiotemporal graphs"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "shared autonomy via deep reinforcement learning"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-3 (induced by source paper "better than demonstrator imitation learning via automatically ranked demonstrations"):
  with members:
    - "inverse reinforcement learning with multiple ranked experts"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "imitation learning from imperfect demonstration"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "risk aware active inverse reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "an algorithmic perspective on imitation learning"
    - "ensembledagger a bayesian approach to safe imitation learning"

Connected through hyperedge HE-4 (induced by source paper "uncertainty aware data aggregation for deep imitation learning"):
  with members:
    - "the best of both worlds combining recent advances in neural machine translation"
    - "carla an open urban driving simulator"
    - "learning robust bed making using deep imitation learning with dart"
    - "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"
    - "deep bayesian active learning with image data"
    - "end to end driving via conditional imitation learning"
    - "deep exploration via bootstrapped dqn"

Connected through hyperedge HE-5 (induced by source paper "perceptual attention based predictive control"):
  with members:
    - "ensemble bayesian decision making with redundant deep perceptual control policies"
    - "autorally an open platform for aggressive autonomous driving"
    - "agile autonomous driving using end to end deep imitation learning"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "mask r cnn"
    - "end to end learning for self driving cars"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"

Connected through hyperedge HE-6 (induced by source paper "ensemble bayesian decision making with redundant deep perceptual control policies"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial attacks on neural network policies"
    - "agile autonomous driving using end to end deep imitation learning"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "end to end learning for self driving cars"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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.LG(Machine Learning)