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: "on a formal model of safe and scalable self driving cars"
  Abstract: In recent years, car makers and tech companies have been racing towards self driving cars. It seems that the main parameter in this race is who will have the first car on the road. The goal of this paper is to add to the equation two additional crucial parameters. The first is standardization of safety assurance --- what are the minimal requirements that every self-driving car must satisfy, and how can we verify these requirements. The second parameter is scalability --- engineering solutions that lead to unleashed costs will not scale to millions of cars, which will push interest in this field into a niche academic corner, and drive the entire field into a "winter of autonomous driving". In the first part of the paper we propose a white-box, interpretable, mathematical model for safety assurance, which we call Responsibility-Sensitive Safety (RSS). In the second part we describe a design of a system that adheres to our safety assurance requirements and is scalable to millions of cars.

Connected through hyperedge HE-1 (induced by source paper "training adversarial agents to exploit weaknesses in deep control policies"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "adaptive stress testing for autonomous vehicles"
    - "a survey of deep learning applications to autonomous vehicle control"
    - "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "adversarially robust generalization requires more data"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles"

Connected through hyperedge HE-2 (induced by source paper "a deep reinforcement learning based driving policy for autonomous road vehicles"):
  with members:
    - "elements of effective deep reinforcement learning towards tactical driving decision making"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "limits of end to end learning"
    - "towards full automated drive in urban environments a demonstration in gomentum station california"
    - "brain inspired cognitive model with attention for self driving cars"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "end to end learning of driving models from large scale video datasets"

Connected through hyperedge HE-3 (induced by source paper "towards learning multi agent negotiations via self play"):
  with members:
    - "model free deep reinforcement learning for urban autonomous driving"
    - "carla an open urban driving simulator"
    - "emergent complexity via multi agent competition"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "end to end driving via conditional imitation learning"
    - "proximal policy optimization algorithms"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-4 (induced by source paper "provably efficient reconstruction of policy networks"):
  with members:
    - "safe policy improvement with an estimated baseline policy"
    - "attraction repulsion actor critic for continuous control reinforcement learning"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "fourier policy gradients"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"

Connected through hyperedge HE-5 (induced by source paper "tackling existence probabilities of objects with motion planning for automated urban driving"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "occlusion aware risk assessment for autonomous driving in urban environments"
    - "tackling occlusions limited sensor range with set based safety verification"
    - "limited visibility and uncertainty aware motion planning for automated driving"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"

Connected through hyperedge HE-6 (induced by source paper "efficient uncertainty aware decision making for automated driving using guided branching"):
  with members:
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "safe trajectory generation for complex urban environments using spatio temporal semantic corridor"
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "despot online pomdp planning with regularization"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-7 (induced by source paper "modeling sensing and perception errors towards robust decision making in autonomous vehicles"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "out of sight but not out of mind an answer set programming based online abduction framework for visual sensemaking in autonomous driving"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "concrete problems in ai safety"

Connected through hyperedge HE-8 (induced by source paper "efficient statistical validation with edge cases to evaluate highly automated vehicles"):
  with members:
    - "carla an open urban driving simulator"
    - "playing atari with deep 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.RO(Robotics)