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: "counterfactual explanations without opening the black box automated decisions and the gdpr"
  Abstract: There has been much discussion of the right to explanation in the EU General Data Protection Regulation, and its existence, merits, and disadvantages. Implementing a right to explanation that opens the black box of algorithmic decision-making faces major legal and technical barriers. Explaining the functionality of complex algorithmic decision-making systems and their rationale in specific cases is a technically challenging problem. Some explanations may offer little meaningful information to data subjects, raising questions around their value. Explanations of automated decisions need not hinge on the general public understanding how algorithmic systems function. Even though such interpretability is of great importance and should be pursued, explanations can, in principle, be offered without opening the black box. Looking at explanations as a means to help a data subject act rather than merely understand, one could gauge the scope and content of explanations according to the specific goal or action they are intended to support. From the perspective of individuals affected by automated decision-making, we propose three aims for explanations: (1) to inform and help the individual understand why a particular decision was reached, (2) to provide grounds to contest the decision if the outcome is undesired, and (3) to understand what would need to change in order to receive a desired result in the future, based on the current decision-making model. We assess how each of these goals finds support in the GDPR. We suggest data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims. These counterfactual explanations describe the smallest change to the world that can be made to obtain a desirable outcome, or to arrive at the closest possible world, without needing to explain the internal logic of the system.

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"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "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"

Connected through hyperedge HE-3 (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"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "measurable counterfactual local explanations for any classifier"
    - "enriching visual with verbal explanations for relational concepts combining lime with aleph"

Connected through hyperedge HE-4 (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-5 (induced by source paper "why is chicago deceptive towards building model driven tutorials for humans"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "fairness by explicability and adversarial shap learning"):
  with members:
    - "counterfactual fairness removing direct effects through regularization"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "flexible and context specific ai explainability a multidisciplinary approach"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "functional transparency for structured data a game theoretic approach"
    - "towards robust locally linear deep networks"
    - "explaining a black box by using a deep variational information bottleneck approach"
    - "learning a sat solver from single bit supervision"
    - "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"
    - "contextual explanation networks"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-8 (induced by source paper "no computation without representation avoiding data and algorithm biases through diversity"):
  with members:
    - "problem formulation and fairness"
    - "efficient search for diverse coherent explanations"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "crowdsourcing with fairness diversity and budget constraints"
    - "the social cost of strategic classification"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "creating fair models of atherosclerotic cardiovascular disease risk"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"

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)