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: "rigorous agent evaluation an adversarial approach to uncover catastrophic failures"
  Abstract: This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus on two problems: searching for scenarios when learned agents fail and assessing their probability of failure. The standard method for agent evaluation in reinforcement learning, Vanilla Monte Carlo, can miss failures entirely, leading to the deployment of unsafe agents. We demonstrate this is an issue for current agents, where even matching the compute used for training is sometimes insufficient for evaluation. To address this shortcoming, we draw upon the rare event probability estimation literature and propose an adversarial evaluation approach. Our approach focuses evaluation on adversarially chosen situations, while still providing unbiased estimates of failure probabilities. The key difficulty is in identifying these adversarial situations -- since failures are rare there is little signal to drive optimization. To solve this we propose a continuation approach that learns failure modes in related but less robust agents. Our approach also allows reuse of data already collected for training the agent. We demonstrate the efficacy of adversarial evaluation on two standard domains: humanoid control and simulated driving. Experimental results show that our methods can find catastrophic failures and estimate failures rates of agents multiple orders of magnitude faster than standard evaluation schemes, in minutes to hours rather than days.

Connected through hyperedge HE-1 (induced by source paper "finding and visualizing weaknesses of deep reinforcement learning agents"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial policies attacking deep reinforcement learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "establishing appropriate trust via critical states"
    - "sanity checks for saliency maps"
    - "understanding regularization to visualize convolutional neural networks"
    - "evaluating feature importance estimates"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-2 (induced by source paper "analysing deep reinforcement learning agents trained with domain randomisation"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "learning dexterous in hand manipulation"
    - "an introduction to deep reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "predicting model failure using saliency maps in autonomous driving systems"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "visualbackprop efficient visualization of cnns"
    - "evaluating uncertainty quantification in end to end autonomous driving control"
    - "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"
    - "a human grounded evaluation benchmark for local explanations of machine learning"
    - "the elephant in the room"
    - "not using the car to see the sidewalk quantifying and controlling the effects of context in classification and segmentation"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-4 (induced by source paper "dependable neural networks for safety critical tasks"):
  with members:
    - "compound domain adaptation in an open world"
    - "a survey of deep learning techniques for autonomous driving"
    - "automatic adaptation of object detectors to new domains using self training"
    - "towards physically safe reinforcement learning under supervision"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "task embedded control networks for few shot imitation learning"
    - "dlow domain flow for adaptation and generalization"

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)