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 reinforcement learning with model uncertainty estimates"
  Abstract: Many current autonomous systems are being designed with a strong reliance on black box predictions from deep neural networks (DNNs). However, DNNs tend to be overconfident in predictions on unseen data and can give unpredictable results for far-from-distribution test data. The importance of predictions that are robust to this distributional shift is evident for safety-critical applications, such as collision avoidance around pedestrians. Measures of model uncertainty can be used to identify unseen data, but the state-of-the-art extraction methods such as Bayesian neural networks are mostly intractable to compute. This paper uses MC-Dropout and Bootstrapping to give computationally tractable and parallelizable uncertainty estimates. The methods are embedded in a Safe Reinforcement Learning framework to form uncertainty-aware navigation around pedestrians. The result is a collision avoidance policy that knows what it does not know and cautiously avoids pedestrians that exhibit unseen behavior. The policy is demonstrated in simulation to be more robust to novel observations and take safer actions than an uncertainty-unaware baseline.

Connected through hyperedge HE-1 (induced by source paper "estimating risk and uncertainty in deep reinforcement learning"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "exploration by random network distillation"
    - "measuring and characterizing generalization in deep reinforcement learning"
    - "information directed exploration for deep reinforcement learning"
    - "mitigating planner overfitting in model based reinforcement learning"
    - "exploration by distributional reinforcement learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-2 (induced by source paper "visual based autonomous driving deployment from a stochastic and uncertainty aware perspective"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "real to virtual domain unification for end to end autonomous driving"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "multimodal unsupervised image to image translation"
    - "drit diverse image to image translation via disentangled representations"
    - "virtual to real reinforcement learning for autonomous driving"
    - "uncertainty aware learning from demonstration using mixture density networks with sampling free variance modeling"

Connected through hyperedge HE-3 (induced by source paper "fully bayesian recurrent neural networks for safe reinforcement learning"):
  with members:
    - "model free deep reinforcement learning for urban autonomous driving"
    - "social attention modeling attention in human crowds"
    - "bayesian recurrent neural networks"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "concrete problems in ai safety"
    - "safe multi agent reinforcement learning for autonomous driving"

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)