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 in car following maneuvers"
  Abstract: The safety of Automated Vehicles (AVs) must be assured before their release and deployment. The current approach to evaluation relies primarily on (i) testing AVs on public roads or (ii) track testing with scenarios defined in a test matrix. These two methods have completely opposing drawbacks: the former, while offering realistic scenarios, takes too much time to execute; the latter, though it can be completed in a short amount of time, has no clear correlation to safety benefits in the real world. To avoid the aforementioned problems, we propose Accelerated Evaluation, focusing on the car-following scenario. The stochastic human-controlled vehicle (HV) motions are modeled based on 1.3 million miles of naturalistic driving data collected by the University of Michigan Safety Pilot Model Deployment Program. The statistics of the HV behaviors are then modified to generate more intense interactions between HVs and AVs to accelerate the evaluation procedure. The Importance Sampling theory was used to ensure that the safety benefits of AVs are accurately assessed under accelerated tests. Crash, injury and conflict rates for a simulated AV are simulated to demonstrate the proposed approach. Results show that test duration is reduced by a factor of 300 to 100,000 compared with the non-accelerated (naturalistic) evaluation. In other words, the proposed techniques have great potential for accelerating the AV evaluation process.

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 "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"
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "end to end learning for self driving cars"
    - "query efficient imitation learning for end to end autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "testing scenario library generation for connected and automated vehicles part ii case studies"):
  with members:
    - "testing scenario library generation for connected and automated vehicles part i methodology"
    - "evaluation of the energy efficiency in a mixed traffic with automated vehicles and human controlled vehicles"
    - "end to end learning for self driving cars"
    - "query efficient imitation learning for end to end autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "performance boundary identification for the evaluation of automated vehicles using gaussian process classification"):
  with members:
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "towards affordable on track testing for autonomous vehicle a kriging based statistical approach"

Connected through hyperedge HE-5 (induced by source paper "time and energy optimal lane change maneuvers for cooperating connected automated vehicles"):
  with members:
    - "a learning based stochastic mpc design for cooperative adaptive cruise control to handle interfering vehicles"
    - "a learning based framework for two dimensional vehicle maneuver prediction over v2v networks"

Connected through hyperedge HE-6 (induced by source paper "scalable end to end autonomous vehicle testing via rare event simulation"):
  with members:
    - "carla an open urban driving simulator"
    - "imitating driver behavior with generative adversarial networks"
    - "spectrally normalized margin bounds for neural networks"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "on a formal model of safe and scalable self driving cars"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "the social dilemma of autonomous vehicles"
    - "deep exploration via bootstrapped dqn"

Connected through hyperedge HE-7 (induced by source paper "cluster naturalistic driving encounters using deep unsupervised learning"):
  with members:
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "autoencoder regularized network for driving style representation learning"
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "trafficnet an open naturalistic driving scenario library"
    - "empirical study of dsrc performance based on safety pilot model deployment data"
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"

Connected through hyperedge HE-8 (induced by source paper "extracting v2v encountering scenarios from naturalistic driving database"):
  with members:
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "empirical study of dsrc performance based on safety pilot model deployment data"

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.OH(Other Computer Science)