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: "the social dilemma of autonomous vehicles"
  Abstract: Autonomous vehicles (AVs) should reduce traffic accidents, but they will sometimes have to choose between two evils, such as running over pedestrians or sacrificing themselves and their passenger to save the pedestrians. Defining the algorithms that will help AVs make these moral decisions is a formidable challenge. We found that participants in six Amazon Mechanical Turk studies approved of utilitarian AVs (that is, AVs that sacrifice their passengers for the greater good) and would like others to buy them, but they would themselves prefer to ride in AVs that protect their passengers at all costs. The study participants disapprove of enforcing utilitarian regulations for AVs and would be less willing to buy such an AV. Accordingly, regulating for utilitarian algorithms may paradoxically increase casualties by postponing the adoption of a safer technology.

Connected through hyperedge HE-1 (induced by source paper "task motion planning for safe and efficient urban driving"):
  with members:
    - "interpretable end to end urban autonomous driving with latent deep reinforcement learning"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "learning from interventions using hierarchical policies for safe learning"
    - "augmenting knowledge through statistical goal oriented human robot dialog"
    - "learning to guide task and motion planning using score space representation"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "carla an open urban driving simulator"
    - "combining neural networks and tree search for task and motion planning in challenging environments"

Connected through hyperedge HE-2 (induced by source paper "beyond near and long term towards a clearer account of research priorities in ai ethics and society"):
  with members:
    - "defining and unpacking transformative ai"

Connected through hyperedge HE-3 (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"
    - "an analysis of iso 26262 using machine learning safely in automotive software"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving crowd guided ai decision making in ethical dilemmas"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "deep tractable probabilistic models for moral responsibility"
    - "building ethics into artificial intelligence"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "a voting based system for ethical decision making"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "risk aware reasoning for autonomous vehicles"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "predicting driver attention in critical situations"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "training a network to attend like human drivers saves it from common but misleading loss functions"
    - "shallow and deep convolutional networks for saliency prediction"
    - "instance level segmentation for autonomous driving with deep densely connected mrfs"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"

Connected through hyperedge HE-6 (induced by source paper "assessing the safety and reliability of autonomous vehicles from road testing"):
  with members:
    - "probabilistic model checking of robots deployed in extreme environments"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "formal verification of autonomous vehicle platooning"

Connected through hyperedge HE-7 (induced by source paper "the role of artificial intelligence in achieving the sustainable development goals"):
  with members:
    - "automatically identifying counting and describing wild animals in camera trap images with deep learning"
    - "the mythos of model interpretability"
    - "research priorities for robust and beneficial artificial intelligence"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-8 (induced by source paper "enabling value sensitive ai systems through participatory design fictions"):
  with members:
    - "cooperative inverse reinforcement 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.CY(Computers and Society)