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: "explaining machine learning classifiers through diverse counterfactual explanations"
  Abstract: Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions. An intriguing class of explanations is through counterfactuals, hypothetical examples that show people how to obtain a different prediction. We posit that effective counterfactual explanations should satisfy two properties: feasibility of the counterfactual actions given user context and constraints, and diversity among the counterfactuals presented. To this end, we propose a framework for generating and evaluating a diverse set of counterfactual explanations based on determinantal point processes. To evaluate the actionability of counterfactuals, we provide metrics that enable comparison of counterfactual-based methods to other local explanation methods. We further address necessary tradeoffs and point to causal implications in optimizing for counterfactuals. Our experiments on four real-world datasets show that our framework can generate a set of counterfactuals that are diverse and well approximate local decision boundaries, outperforming prior approaches to generating diverse counterfactuals. We provide an implementation of the framework at https://github.com/microsoft/DiCE.

Connected through hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explainable reinforcement learning through a causal lens"
    - "learning interpretable models with causal guarantees"
    - "explaining classifiers with causal concept effect cace"
    - "neural network attributions a causal perspective"
    - "on the accuracy of influence functions for measuring group effects"
    - "explaining deep learning models with constrained adversarial examples"
    - "incorporating interpretability into latent factor models via fast influence analysis"

Connected through hyperedge HE-2 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences"):
  with members:
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "personalized explanation in machine learning"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "explaining models an empirical study of how explanations impact fairness judgment"
    - "the challenge of crafting intelligible intelligence"
    - "metrics for explainable ai challenges and prospects"
    - "a survey of evaluation methods and measures for interpretable machine learning"

Connected through hyperedge HE-3 (induced by source paper "why is chicago deceptive towards building model driven tutorials for humans"):
  with members:
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "a human grounded evaluation of shap for alert processing"
    - "rating reliability and bias in news articles does ai assistance help everyone"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "efficient search for diverse coherent explanations"
    - "local rule based explanations of black box decision systems"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "fairness by explicability and adversarial shap learning"):
  with members:
    - "counterfactual fairness removing direct effects through regularization"
    - "a framework for understanding unintended consequences of machine learning"
    - "a survey on bias and fairness in machine learning"
    - "explainable ai for trees from local explanations to global understanding"
    - "a neural network framework for fair classifier"
    - "discriminative but not discriminatory a comparison of fairness definitions under different worldviews"
    - "mitigating unwanted biases with adversarial learning"
    - "classification with fairness constraints a meta algorithm with provable guarantees"

Connected through hyperedge HE-5 (induced by source paper "decisions counterfactual explanations and strategic behavior"):
  with members:
    - "optimal decision making under strategic behavior"
    - "fair decisions despite imperfect predictions"
    - "strategic adaptation to classifiers a causal perspective"
    - "the disparate effects of strategic manipulation"
    - "efficient search for diverse coherent explanations"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "how do classifiers induce agents to invest effort strategically"
    - "a short term intervention for long term fairness in the labor market"

Connected through hyperedge HE-6 (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"
    - "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"
    - "face feasible and actionable counterfactual explanations"
    - "efficient search for diverse coherent explanations"
    - "model agnostic counterfactual explanations for consequential decisions"

Connected through hyperedge HE-7 (induced by source paper "vice visual counterfactual explanations for machine learning models"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "a survey of methods for explaining black box models"
    - "manipulating and measuring model interpretability"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "the hidden assumptions behind counterfactual explanations and principal reasons"):
  with members:
    - "efficient search for diverse coherent explanations"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "delayed impact of fair machine learning"
    - "interpretable credit application predictions with counterfactual explanations"
    - "how do classifiers induce agents to invest effort strategically"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "generating counterfactual explanations with natural language"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

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)