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: "deep predictive models for collision risk assessment in autonomous driving"
  Abstract: In this paper, we investigate a predictive approach for collision risk assessment in autonomous and assisted driving. A deep predictive model is trained to anticipate imminent accidents from traditional video streams. In particular, the model learns to identify cues in RGB images that are predictive of hazardous upcoming situations. In contrast to previous work, our approach incorporates (a) temporal information during decision making, (b) multi-modal information about the environment, as well as the proprioceptive state and steering actions of the controlled vehicle, and (c) information about the uncertainty inherent to the task. To this end, we discuss Deep Predictive Models and present an implementation using a Bayesian Convolutional LSTM. Experiments in a simple simulation environment show that the approach can learn to predict impending accidents with reasonable accuracy, especially when multiple cameras are used as input sources.

Connected through hyperedge HE-1 (induced by source paper "requirements driven test generation for autonomous vehicles with machine learning components"):
  with members:
    - "counterexample guided data augmentation"
    - "semantic adversarial deep learning"
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "carla an open urban driving simulator"
    - "feature guided black box safety testing of deep neural networks"
    - "on a formal model of safe and scalable self driving cars"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-2 (induced by source paper "simulation based adversarial test generation for autonomous vehicles with machine learning components"):
  with members:
    - "semantic adversarial deep learning"
    - "output range analysis for deep neural networks"
    - "systematic testing of convolutional neural networks for autonomous driving"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "on a formal model of safe and scalable self driving cars"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-3 (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"
    - "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"
    - "dropoutdagger a bayesian approach to safe imitation learning"
    - "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.RO(Robotics)