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: "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
  Abstract: Automated vehicles (AVs) must be thoroughly evaluated before their release and deployment. A widely used evaluation approach is the Naturalistic-Field Operational Test (N-FOT), which tests prototype vehicles directly on the public roads. Due to the low exposure to safety-critical scenarios, N-FOTs are time consuming and expensive to conduct. In this paper, we propose an accelerated evaluation approach for AVs. The results can be used to generate motions of the other primary vehicles to accelerate the verification of AVs in simulations and controlled experiments. Frontal collision due to unsafe cut-ins is the target crash type of this paper. Human-controlled vehicles making unsafe lane changes are modeled as the primary disturbance to AVs based on data collected by the University of Michigan Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behaviors, which generate risky testing scenarios while preserving the statistical information so that the safety benefits of AVs in nonaccelerated cases can be accurately estimated. The cross-entropy method is used to recursively search for the optimal skewing parameters. The frequencies of the occurrences of conflicts, crashes, and injuries are estimated for a modeled AV, and the achieved accelerated rate is around 2000 to 20 000. In other words, in the accelerated simulations, driving for 1000 miles will expose the AV with challenging scenarios that will take about 2 to 20 million miles of real-world driving to encounter. This technique thus has the potential to greatly reduce the development and validation time for AVs.

Connected through hyperedge HE-1 (induced by source paper "learning to collide an adaptive safety critical scenarios generating method"):
  with members:
    - "generative adversarial minority oversampling"
    - "active domain randomization"
    - "adversarial autoaugment"
    - "deceptionnet network driven domain randomization"
    - "meta sim learning to generate synthetic datasets"
    - "class balanced loss based on effective number of samples"
    - "fast autoaugment"
    - "cmts conditional multiple trajectory synthesizer for generating safety critical driving scenarios"

Connected through hyperedge HE-2 (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"
    - "deep predictive models for collision risk assessment in autonomous driving"
    - "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-3 (induced by source paper "adaptive stress testing for autonomous vehicles"):
  with members:
    - "deep stochastic radar models"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "on a formal model of safe and scalable self driving cars"
    - "evaluation of automated vehicles in the frontal cut in scenario an enhanced approach using piecewise mixture models"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

Connected through hyperedge HE-4 (induced by source paper "modeling multi vehicle interaction scenarios using gaussian random field"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "interaction aware probabilistic behavior prediction in urban environments"

Connected through hyperedge HE-5 (induced by source paper "testing scenario library generation for connected and automated vehicles part i methodology"):
  with members:
    - "testing scenario library generation for connected and automated vehicles part ii case studies"
    - "end to end learning for self driving cars"
    - "accelerated evaluation of automated vehicles in car following maneuvers"
    - "query efficient imitation learning for end to end autonomous driving"

Connected through hyperedge HE-6 (induced by source paper "a behavior driven approach for sampling rare event situations for autonomous vehicles"):
  with members:
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "scalable end to end autonomous vehicle testing via rare event simulation"
    - "trafficnet an open naturalistic driving scenario library"

Connected through hyperedge HE-7 (induced by source paper "performance boundary identification for the evaluation of automated vehicles using gaussian process classification"):
  with members:
    - "towards affordable on track testing for autonomous vehicle a kriging based statistical approach"
    - "accelerated evaluation of automated vehicles in car following maneuvers"

Connected through hyperedge HE-8 (induced by source paper "real time prediction of intermediate horizon automotive collision risk"):
  with members:
    - "simultaneous policy learning and latent state inference for imitating driver behavior"
    - "imitating driver behavior with generative adversarial networks"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "unified deep supervised domain adaptation and generalization"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "sim to real robot learning from pixels with progressive nets"
    - "domain separation networks"
    - "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)