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: "evaluating uncertainty quantification in end to end autonomous driving control"
  Abstract: A rise in popularity of Deep Neural Networks (DNNs), attributed to more powerful GPUs and widely available datasets, has seen them being increasingly used within safety-critical domains. One such domain, self-driving, has benefited from significant performance improvements, with millions of miles having been driven with no human intervention. Despite this, crashes and erroneous behaviours still occur, in part due to the complexity of verifying the correctness of DNNs and a lack of safety guarantees. #R##N#In this paper, we demonstrate how quantitative measures of uncertainty can be extracted in real-time, and their quality evaluated in end-to-end controllers for self-driving cars. To this end we utilise a recent method for gathering approximate uncertainty information from DNNs without changing the network's architecture. We propose evaluation techniques for the uncertainty on two separate architectures which use the uncertainty to predict crashes up to five seconds in advance. We find that mutual information, a measure of uncertainty in classification networks, is a promising indicator of forthcoming crashes.

Connected through hyperedge HE-1 (induced by source paper "deep neural network perception models and robust autonomous driving systems"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "one pixel attack for fooling deep neural networks"
    - "self driving cars a survey"
    - "adversarially robust distillation"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "a survey of autonomous driving common practices and emerging technologies"
    - "panoptic deeplab a simple strong and fast baseline for bottom up panoptic segmentation"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "adversarial machine learning perspectives from adversarial risk analysis"):
  with members:
    - "estimating counterfactual treatment outcomes over time through adversarially balanced representations"
    - "gradient methods for solving stackelberg games"
    - "opponent aware reinforcement learning"
    - "knowing when to stop evaluation and verification of conformity to output size specifications"
    - "gltr statistical detection and visualization of generated text"
    - "a survey of game theoretic approaches for adversarial machine learning in cybersecurity tasks"
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"

Connected through hyperedge HE-3 (induced by source paper "fast predictive uncertainty for classification with bayesian deep networks"):
  with members:
    - "backpack packing more into backprop"
    - "deep learning for classical japanese literature"
    - "evidential deep learning to quantify classification uncertainty"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-4 (induced by source paper "practical solutions for machine learning safety in autonomous vehicles"):
  with members:
    - "predicting model failure using saliency maps in autonomous driving systems"
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "selectivenet a deep neural network with an integrated reject option"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "using pre training can improve model robustness and uncertainty"
    - "training verified learners with learned verifiers"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"

Connected through hyperedge HE-5 (induced by source paper "generalizing from a few environments in safety critical reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "generalization and regularization in dqn"
    - "fingerprint policy optimisation for robust reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "ai safety gridworlds"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "predicting model failure using saliency maps in autonomous driving systems"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "visualbackprop efficient visualization of cnns"
    - "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"
    - "a human grounded evaluation benchmark for local explanations of machine learning"
    - "the elephant in the room"
    - "rigorous agent evaluation an adversarial approach to uncover catastrophic failures"
    - "not using the car to see the sidewalk quantifying and controlling the effects of context in classification and segmentation"
    - "sanity checks for saliency maps"

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)