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 ai beware of inmates running the asylum or how i learnt to stop worrying and love the social and behavioural sciences"
  Abstract: In his seminal book `The Inmates are Running the Asylum: Why High-Tech Products Drive Us Crazy And How To Restore The Sanity' [2004, Sams Indianapolis, IN, USA], Alan Cooper argues that a major reason why software is often poorly designed (from a user perspective) is that programmers are in charge of design decisions, rather than interaction designers. As a result, programmers design software for themselves, rather than for their target audience, a phenomenon he refers to as the `inmates running the asylum'. This paper argues that explainable AI risks a similar fate. While the re-emergence of explainable AI is positive, this paper argues most of us as AI researchers are building explanatory agents for ourselves, rather than for the intended users. But explainable AI is more likely to succeed if researchers and practitioners understand, adopt, implement, and improve models from the vast and valuable bodies of research in philosophy, psychology, and cognitive science, and if evaluation of these models is focused more on people than on technology. From a light scan of literature, we demonstrate that there is considerable scope to infuse more results from the social and behavioural sciences into explainable AI, and present some key results from these fields that are relevant to explainable 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 "one explanation does not fit all the promise of interactive explanations for machine learning transparency"):
  with members:
    - "explainability fact sheets a framework for systematic assessment of explainable approaches"
    - "blimey surrogate prediction explanations beyond lime"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "personalized explanation in machine learning a conceptualization"
    - "face feasible and actionable counterfactual explanations"
    - "natural language interaction with explainable ai models"
    - "the challenge of crafting intelligible intelligence"
    - "explanation in artificial intelligence insights from the social sciences"

Connected through hyperedge HE-3 (induced by source paper "explaining explanations in ai"):
  with members:
    - "high dimensional spaces deep learning and adversarial examples"
    - "methods for interpreting and understanding deep neural networks"
    - "interpretable explorable approximations of black box models"
    - "explanation in artificial intelligence insights from the social sciences"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-4 (induced by source paper "teaching ai to explain its decisions using embeddings and multi task learning"):
  with members:
    - "ted teaching ai to explain its decisions"
    - "methods for interpreting and understanding deep neural networks"
    - "collaborative human ai chai evidence based interpretable melanoma classification in dermoscopic images"
    - "boolean decision rules via column generation"
    - "teaching meaningful explanations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "a unified approach to interpreting model predictions"
    - "a formal framework to characterize interpretability of procedures"

Connected through hyperedge HE-5 (induced by source paper "why does my model fail contrastive local explanations for retail forecasting"):
  with members:
    - "efficient search for diverse coherent explanations"
    - "explainable ai for trees from local explanations to global understanding"
    - "grounding visual explanations"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "a survey of methods for explaining black box models"
    - "finding influential training samples for gradient boosted decision trees"
    - "explanation in artificial intelligence insights from the social sciences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

Connected through hyperedge HE-6 (induced by source paper "face feasible and actionable counterfactual explanations"):
  with members:
    - "efficient search for diverse coherent explanations"
    - "delayed impact of fair machine learning"
    - "model agnostic supervised local explanations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "shortest path distance in random k nearest neighbor graphs"

Connected through hyperedge HE-7 (induced by source paper "a grounded interaction protocol for explainable artificial intelligence"):
  with members:
    - "plan explanations as model reconciliation an empirical study"
    - "explanation in artificial intelligence insights from the social sciences"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"

Connected through hyperedge HE-8 (induced by source paper "contrastive explanations for large errors in retail forecasting predictions through monte carlo simulations"):
  with members:
    - "why should i trust you explaining the predictions of any classifier"

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)