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: "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
  Abstract: In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

Connected through hyperedge HE-1 (induced by source paper "ai safety state of the field through quantitative lens"):
  with members:
    - "fairness in learning based sequential decision algorithms a survey"
    - "dave i can assure you that it s going to be all right a definition case for and survey of algorithmic assurances in human autonomy trust relationships"
    - "explainability in human agent systems"
    - "classification schemas for artificial intelligence failures"
    - "a survey on bias and fairness in machine learning"
    - "machine learning testing survey landscapes and horizons"
    - "unexplainability and incomprehensibility of artificial intelligence"
    - "from what to how an initial review of publicly available ai ethics tools methods and research to translate principles into practices"

Connected through hyperedge HE-2 (induced by source paper "survlime a method for explaining machine learning survival models"):
  with members:
    - "explaining the explainer a first theoretical analysis of lime"
    - "graphlime local interpretable model explanations for graph neural networks"
    - "evaluating explainers via perturbation"
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "alime autoencoder based approach for local interpretability"
    - "measurable counterfactual local explanations for any classifier"
    - "enriching visual with verbal explanations for relational concepts combining lime with aleph"
    - "dlime a deterministic local interpretable model agnostic explanations approach for computer aided diagnosis systems"

Connected through hyperedge HE-3 (induced by source paper "towards ground truth evaluation of visual explanations"):
  with members:
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "explicit bias discovery in visual question answering models"
    - "methods for interpreting and understanding deep neural networks"
    - "sanity checks for saliency maps"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "a simple neural network module for relational reasoning"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-4 (induced by source paper "from data to actions in intelligent transportation systems a prescription of functional requirements for model actionability"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "predicting transportation modes of gps trajectories using feature engineering and noise removal"
    - "explanation methods in deep learning users values concerns and challenges"
    - "transferable pedestrian motion prediction models at intersections"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "understanding traffic density from large scale web camera data"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "when traffic flow prediction meets wireless big data analytics"

Connected through hyperedge HE-5 (induced by source paper "explainable agents through social cues a review"):
  with members:
    - "explainability in human agent systems"
    - "interactively shaping robot behaviour with unlabeled human instructions"
    - "expressing robot incapability"
    - "explicability legibility predictability transparency privacy security the emerging landscape of interpretable agent behavior"
    - "expressive robot motion timing"
    - "enabling robots to communicate their objectives"
    - "balancing explicability and explanation in human aware planning"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"

Connected through hyperedge HE-6 (induced by source paper "lunar cellular automata for drifting data streams"):
  with members:
    - "spiking neural networks and online learning an overview and perspectives"
    - "evaluating and characterizing incremental learning from non stationary data"
    - "scikit multiflow a multi output streaming framework"
    - "a survey of cellular automata types dynamics non uniformity and applications"
    - "characterizing concept drift"
    - "why should i trust you explaining the predictions of any classifier"
    - "swarms on continuous data"

Connected through hyperedge HE-7 (induced by source paper "explainable artificial intelligence and machine learning a reality rooted perspective"):
  with members:
    - "explaining explanations in ai"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "explaining explanations an overview of interpretability of machine learning"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "what do we need to build explainable ai systems for the medical domain"
    - "the mythos of model interpretability"
    - "building machines that learn and think like people"

Connected through hyperedge HE-8 (induced by source paper "towards transparent robotic planning via contrastive explanations"):
  with members:
    - "explaining explanations in ai"
    - "contrastive explanation a structural model approach"
    - "establishing appropriate trust via critical states"
    - "counterexamples for robotic planning explained in structured language"
    - "explanation in artificial intelligence insights from the social sciences"

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.AI(Artificial Intelligence)