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 the safety of machine learning cyber physical systems decision sciences and data products"
  Abstract: Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine learning. Heretofore, the definition of safety has not been formalized in a machine learning context. In this paper, we do so by defining machine learning safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. We then use this definition to examine safety in all sorts of applications in cyber-physical systems, decision sciences, and data products. We find that the foundational principle of modern statistical machine learning, empirical risk minimization, is not always a sufficient objective. Finally, we discuss how four different categories of strategies for achieving safety in engineering, including inherently safe design, safety reserves, safe fail, and procedural safeguards can be mapped to a machine learning context. We then discuss example techniques that can be adopted in each category, such as considering interpretability and causality of predictive models, objective functions beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software and open data.

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 "data invariants on trust in data driven systems"):
  with members:
    - "tutorial safe and reliable machine learning"
    - "example driven query intent discovery abductive reasoning using semantic similarity"
    - "data sanity check for deep learning systems via learnt assertions"
    - "improving reconstruction autoencoder out of distribution detection with mahalanobis distance"
    - "on calibration of modern neural networks"
    - "learning representations and generative models for 3d point clouds"
    - "effective and complete discovery of order dependencies via set based axiomatization"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "aleatoric and epistemic uncertainty with random forests"):
  with members:
    - "epistemic uncertainty sampling"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "interpretable adversarial perturbation in input embedding space for text"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-4 (induced by source paper "a survey of deep learning applications to autonomous vehicle control"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "imminent collision mitigation with reinforcement learning and vision"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "causal confusion in imitation learning"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "constrained deep neural network architecture search for iot devices accounting hardware calibration"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-5 (induced by source paper "explanation in human ai systems a literature meta review synopsis of key ideas and publications and bibliography for explainable ai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "ted teaching ai to explain its decisions"
    - "explaining explanations an overview of interpretability of machine learning"
    - "deep learning a critical appraisal"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "explainable artificial intelligence xai the goodness criteria and the grasp ability test"
    - "interpretable explorable approximations of black box models"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "understanding artificial intelligence ethics and safety"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "model cards for model reporting"
    - "a framework for understanding unintended consequences of machine learning"
    - "spectrum data poisoning with adversarial deep learning"
    - "explaining explanations in ai"
    - "problem formulation and fairness"

Connected through hyperedge HE-7 (induced by source paper "diagnostic curves for black box models"):
  with members:
    - "tutorial safe and reliable machine learning"
    - "regulatory markets for ai safety"
    - "explaining deep learning models a bayesian non parametric approach"
    - "local rule based explanations of black box decision systems"
    - "representer point selection for explaining deep neural networks"
    - "interpreting neural network judgments via minimal stable and symbolic corrections"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "learning to explain an information theoretic perspective on model interpretation"

Connected through hyperedge HE-8 (induced by source paper "transparency motivations and challenges"):
  with members:
    - "emergent communication in a multi modal multi step referential game"
    - "axiomatic attribution for deep networks"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "emergence of grounded compositional language in multi agent populations"
    - "living together mind and machine intelligence"
    - "challenges for transparency"
    - "visualizing deep neural network decisions prediction difference analysis"

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.CY(Computers and Society)