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: "ensemble bayesian decision making with redundant deep perceptual control policies"
  Abstract: This work presents a novel ensemble of Bayesian Neural Networks (BNNs) for control of safety-critical systems. Decision making for safety-critical systems is challenging due to performance requirements with significant consequences in the event of failure. In practice, failure of such systems can be avoided by introducing redundancies of control. Neural Networks (NNs) are generally not used for safety-critical systems as they can behave in unexpected ways in response to novel inputs. In addition, there may not be any indication as to when they will fail. BNNs have been recognized for their ability to produce not only viable outputs but also provide a measure of uncertainty in these outputs. This work combines the knowledge of prediction uncertainty obtained from BNNs and ensemble control for a redundant control methodology. Our technique is applied to an agile autonomous driving task. Multiple BNNs are trained to control a vehicle in an end-to-end fashion on different sensor inputs provided by the system. We show that an individual network is successful in maneuvering around the track but crashes in the presence of unforeseen input noise. Our proposed ensemble of BNNs shows successful task performance even in the event of multiple sensor failures.

Connected through hyperedge HE-1 (induced by source paper "a general framework for uncertainty estimation in deep learning"):
  with members:
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "where is the information in a deep neural network"
    - "lightweight probabilistic deep networks"
    - "deep drone racing learning agile flight in dynamic environments"
    - "the limits and potentials of deep learning for robotics"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "beauty and the beast optimal methods meet learning for drone racing"

Connected through hyperedge HE-2 (induced by source paper "perceptual attention based predictive control"):
  with members:
    - "autorally an open platform for aggressive autonomous driving"
    - "safe end to end imitation learning for model predictive control"
    - "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-3 (induced by source paper "uncertainty quantification with statistical guarantees in end to end autonomous driving control"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "statistical guarantees for the robustness of bayesian neural networks"
    - "central limit model checking"
    - "robustness guarantees for bayesian inference with gaussian processes"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "carla an open urban driving simulator"
    - "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"

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.RO(Robotics)