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: "face feasible and actionable counterfactual explanations"
  Abstract: Work in Counterfactual Explanations tends to focus on the principle of ``the closest possible world'' that identifies small changes leading to the desired outcome. In this paper we argue that while this approach might initially seem intuitively appealing it exhibits shortcomings not addressed in the current literature. First, a counterfactual example generated by the state-of-the-art systems is not necessarily representative of the underlying data distribution, and may therefore prescribe unachievable goals(e.g., an unsuccessful life insurance applicant with severe disability may be advised to do more sports). Secondly, the counterfactuals may not be based on a ``feasible path'' between the current state of the subject and the suggested one, making actionable recourse infeasible (e.g., low-skilled unsuccessful mortgage applicants may be told to double their salary, which may be hard without first increasing their skill level). These two shortcomings may render counterfactual explanations impractical and sometimes outright offensive. To address these two major flaws, first of all, we propose a new line of Counterfactual Explanations research aimed at providing actionable and feasible paths to transform a selected instance into one that meets a certain goal. Secondly, we propose FACE: an algorithmically sound way of uncovering these ``feasible paths'' based on the shortest path distances defined via density-weighted metrics. Our approach generates counterfactuals that are coherent with the underlying data distribution and supported by the ``feasible paths'' of change, which are achievable and can be tailored to the problem at hand.

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"
    - "the challenge of crafting intelligible intelligence"
    - "a nutritional label for rankings"

Connected through hyperedge HE-2 (induced by source paper "algorithmic recourse from counterfactual explanations to interventions"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "the hidden assumptions behind counterfactual explanations and principal reasons"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "certifai counterfactual explanations for robustness transparency interpretability and fairness of artificial intelligence models"
    - "towards realistic individual recourse and actionable explanations in black box decision making systems"
    - "preserving causal constraints in counterfactual explanations for machine learning classifiers"
    - "efficient search for diverse coherent explanations"
    - "model agnostic counterfactual explanations for consequential decisions"

Connected through hyperedge HE-3 (induced by source paper "convex density constraints for computing plausible counterfactual explanations"):
  with members:
    - "efficient computation of counterfactual explanations of lvq models"
    - "a survey on bias and fairness in machine learning"
    - "interpretable counterfactual explanations guided by prototypes"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "on the computation of counterfactual explanations a survey"
    - "generating contrastive explanations with monotonic attribute functions"
    - "local rule based explanations of black box decision systems"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"

Connected through hyperedge HE-4 (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"
    - "natural language interaction with explainable ai models"
    - "the challenge of crafting intelligible intelligence"
    - "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-5 (induced by source paper "preserving causal constraints in counterfactual explanations for machine learning classifiers"):
  with members:
    - "the hidden assumptions behind counterfactual explanations and principal reasons"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "towards realistic individual recourse and actionable explanations in black box decision making systems"
    - "interpretable counterfactual explanations guided by prototypes"
    - "generative counterfactual introspection for explainable deep learning"
    - "efficient search for diverse coherent explanations"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"

Connected through hyperedge HE-6 (induced by source paper "efficient computation of counterfactual explanations of lvq models"):
  with members:
    - "certifai counterfactual explanations for robustness transparency interpretability and fairness of artificial intelligence models"
    - "interpretable counterfactual explanations guided by prototypes"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "local rule based explanations of black box decision systems"
    - "explaining explanations an overview of interpretability of machine learning"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-7 (induced by source paper "on the computation of counterfactual explanations a survey"):
  with members:
    - "efficient computation of counterfactual explanations of lvq models"
    - "deep learning for audio signal processing"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "a survey of the usages of deep learning in natural language processing"
    - "explaining explanations an overview of interpretability of machine learning"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "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.LG(Machine Learning)