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: "scalable end to end autonomous vehicle testing via rare event simulation"
  Abstract: While recent developments in autonomous vehicle (AV) technology highlight substantial progress, we lack tools for rigorous and scalable testing. Real-world testing, the $\textit{de facto}$ evaluation environment, places the public in danger, and, due to the rare nature of accidents, will require billions of miles in order to statistically validate performance claims. We implement a simulation framework that can test an entire modern autonomous driving system, including, in particular, systems that employ deep-learning perception and control algorithms. Using adaptive importance-sampling methods to accelerate rare-event probability evaluation, we estimate the probability of an accident under a base distribution governing standard traffic behavior. We demonstrate our framework on a highway scenario, accelerating system evaluation by $2$-$20$ times over naive Monte Carlo sampling methods and $10$-$300 \mathsf{P}$ times (where $\mathsf{P}$ is the number of processors) over real-world testing.

Connected through hyperedge HE-1 (induced by source paper "analyzing the variety loss in the context of probabilistic trajectory prediction"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "inferring 3d shapes from image collections using adversarial networks"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "peeking into the future predicting future person activities and locations in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "convolutional social pooling for vehicle trajectory prediction"

Connected through hyperedge HE-2 (induced by source paper "f1 10 an open source autonomous cyber physical platform"):
  with members:
    - "decentralized mpc based obstacle avoidance for multi robot target tracking scenarios"
    - "learning to drive in a day"
    - "sequential circuits from regular expressions revisited"
    - "montre a tool for monitoring timed regular expressions"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "cddt fast approximate 2d ray casting for accelerated localization"
    - "on a formal model of safe and scalable self driving cars"
    - "deep steering learning end to end driving model from spatial and temporal visual cues"

Connected through hyperedge HE-3 (induced by source paper "gladas gesture learning for advanced driver assistance systems"):
  with members:
    - "real time hand gesture detection and classification using convolutional neural networks"
    - "autonomous vehicles that interact with pedestrians a survey of theory and practice"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "carla an open urban driving simulator"
    - "intent communication between autonomous vehicles and pedestrians"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"

Connected through hyperedge HE-4 (induced by source paper "efficient autonomy validation in simulation with adaptive stress testing"):
  with members:
    - "adaptive stress testing for autonomous vehicles"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

Connected through hyperedge HE-5 (induced by source paper "spatial positioning token sptoken for smart mobility"):
  with members:
    - "iota based directed acyclic graphs without orphans"
    - "deeptraffic crowdsourced hyperparameter tuning of deep reinforcement learning systems for multi agent dense traffic navigation"
    - "deconvolving feedback loops in recommender systems"
    - "expert level control of ramp metering based on multi task deep reinforcement learning"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"

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"
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "trafficnet an open naturalistic driving scenario library"

Connected through hyperedge HE-7 (induced by source paper "adaptive stress testing with reward augmentation for autonomous vehicle validation"):
  with members:
    - "adaptive stress testing for autonomous vehicles"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "on a formal model of safe and scalable self driving 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.LG(Machine Learning)