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: "an analysis of iso 26262 using machine learning safely in automotive software"
  Abstract: Machine learning (ML) plays an ever-increasing role in advanced automotive functionality for driver assistance and autonomous operation; however, its adequacy from the perspective of safety certification remains controversial. In this paper, we analyze the impacts that the use of ML as an implementation approach has on ISO 26262 safety lifecycle and ask what could be done to address them. We then provide a set of recommendations on how to adapt the standard to accommodate ML.

Connected through hyperedge HE-1 (induced by source paper "importance driven deep learning system testing"):
  with members:
    - "input prioritization for testing neural networks"
    - "deepfault fault localization for deep neural networks"
    - "machine learning testing survey landscapes and horizons"
    - "formal security analysis of neural networks using symbolic intervals"
    - "symbolic execution for deep neural networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "a survey of deep learning applications to autonomous vehicle control"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "imminent collision mitigation with reinforcement learning and vision"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "causal confusion in imitation learning"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "constrained deep neural network architecture search for iot devices accounting hardware calibration"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-3 (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"
    - "evaluating uncertainty quantification in end to end autonomous driving control"
    - "training verified learners with learned verifiers"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-4 (induced by source paper "deepfault fault localization for deep neural networks"):
  with members:
    - "symbolic execution for deep neural networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "guiding deep learning system testing using surprise adequacy"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "combinatorial testing for deep learning systems"
    - "testing deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "engineering problems in machine learning systems"):
  with members:
    - "improving transparency of deep neural inference process"
    - "towards dependability metrics for neural networks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "runtime monitoring neuron activation patterns"
    - "quantitative projection coverage for testing ml enabled autonomous systems"
    - "towards proving the adversarial robustness of deep neural networks"
    - "maximum resilience of artificial neural networks"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

Connected through hyperedge HE-6 (induced by source paper "deep learning in the automotive industry recent advances and application examples"):
  with members:
    - "co design of deep neural nets and neural net accelerators for embedded vision applications"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "assessment of deep convolutional neural networks for road surface classification"
    - "an anti fraud system for car insurance claim based on visual evidence"
    - "scalable and accurate deep learning for electronic health records"
    - "deeproad gan based metamorphic autonomous driving system testing"
    - "squeezenext hardware aware neural network design"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-7 (induced by source paper "towards structured evaluation of deep neural network supervisors"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"
    - "guiding deep learning system testing using surprise adequacy"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "deeproad gan based metamorphic autonomous driving system testing"
    - "on calibration of modern neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-8 (induced by source paper "algorithmic decision making in avs understanding ethical and technical concerns for smart cities"):
  with members:
    - "governing autonomous vehicles emerging responses for safety liability privacy cybersecurity and industry risks"
    - "semantic foggy scene understanding with synthetic data"
    - "multi radio 5g architecture for connected and autonomous vehicles application and design insights"
    - "assessing and addressing algorithmic bias but before we get there"
    - "autonomous vehicles for smart and sustainable cities an in depth exploration of privacy and cybersecurity implications"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "fairness testing testing software for discrimination"
    - "deeptest automated testing of deep neural network driven autonomous cars"

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.AI(Artificial Intelligence)