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: "stakeholders in explainable ai"
  Abstract: There is general consensus that it is important for artificial#R##N#intelligence (AI) and machine learning systems to be explainable#R##N#and/or interpretable. However, there is no general#R##N#consensus over what is meant by ‘explainable’ and ‘interpretable’.#R##N#In this paper, we argue that this lack of consensus#R##N#is due to there being several distinct stakeholder communities.#R##N#We note that, while the concerns of the individual#R##N#communities are broadly compatible, they are not identical,#R##N#which gives rise to different intents and requirements for explainability/#R##N#interpretability. We use the software engineering#R##N#distinction between validation and verification, and the epistemological#R##N#distinctions between knowns/unknowns, to tease#R##N#apart the concerns of the stakeholder communities and highlight#R##N#the areas where their foci overlap or diverge. It is not#R##N#the purpose of the authors of this paper to ‘take sides’ — we#R##N#count ourselves as members, to varying degrees, of multiple#R##N#communities — but rather to help disambiguate what stakeholders#R##N#mean when they ask ‘Why?’ of an AI.

Connected through hyperedge HE-1 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches"):
  with members:
    - "blimey surrogate prediction explanations beyond lime"
    - "the dangers of post hoc interpretability unjustified counterfactual explanations"
    - "model cards for model reporting"
    - "towards a characterization of explainable systems"
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "personalized explanation in machine learning a conceptualization"
    - "face feasible and actionable counterfactual explanations"
    - "the challenge of crafting intelligible intelligence"

Connected through hyperedge HE-2 (induced by source paper "explainable machine learning in deployment"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "understanding impacts of high order loss approximations and features in deep learning interpretation"
    - "certifai counterfactual explanations for robustness transparency interpretability and fairness of artificial intelligence models"
    - "transparency motivations and challenges"
    - "automatic model monitoring for data streams"
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "neural network attributions a causal perspective"
    - "explaining explanations in ai"

Connected through hyperedge HE-3 (induced by source paper "unexplainability and incomprehensibility of artificial intelligence"):
  with members:
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "personal universes a solution to the multi agent value alignment problem"
    - "explaining explanations in ai"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "unpredictability of ai"
    - "techniques for interpretable machine learning"
    - "emergence of addictive behaviors in reinforcement learning agents"
    - "building safer agi by introducing artificial stupidity"

Connected through hyperedge HE-4 (induced by source paper "towards explainable neural symbolic visual reasoning"):
  with members:
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "women also snowboard overcoming bias in captioning models"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "explanation in artificial intelligence insights from the social sciences"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "logic tensor networks for semantic image interpretation"

Connected through hyperedge HE-5 (induced by source paper "explainable ai for intelligence augmentation in multi domain operations"):
  with members:
    - "discriminating spatial and temporal relevance in deep taylor decompositions for explainable activity recognition"
    - "interpretable to whom a role based model for analyzing interpretable machine learning systems"
    - "uncertainty aware ai ml why and how"
    - "evidential deep learning to quantify classification uncertainty"
    - "probabilistic logic programming with beta distributed random variables"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "why should i trust you explaining the predictions of any classifier"
    - "building machines that learn and think like people"

Connected through hyperedge HE-6 (induced by source paper "quantifying transparency of machine learning systems through analysis of contributions"):
  with members:
    - "model cards for model reporting"
    - "a conceptual architecture for contractual data sharing in a decentralised environment"
    - "packaging and sharing machine learning models via the acumos ai open platform"
    - "a survey on essential components of a self sovereign identity"
    - "defining the collective intelligence supply chain"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "accountability of ai under the law the role of explanation"

Connected through hyperedge HE-7 (induced by source paper "highlighting bias with explainable neural symbolic visual reasoning"):
  with members:
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "women also snowboard overcoming bias in captioning models"
    - "explaining explanations an overview of interpretability of machine learning"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "show and tell a neural image caption generator"

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)