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: "boolean decision rules via column generation"
  Abstract: This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification. An integer program is formulated to optimally trade classification accuracy for rule simplicity. Column generation (CG) is used to efficiently search over an exponential number of candidate clauses (conjunctions or disjunctions) without the need for heuristic rule mining. This approach also bounds the gap between the selected rule set and the best possible rule set on the training data. To handle large datasets, we propose an approximate CG algorithm using randomization. Compared to three recently proposed alternatives, the CG algorithm dominates the accuracy-simplicity trade-off in 7 out of 15 datasets. When maximized for accuracy, CG is competitive with rule learners designed for this purpose, sometimes finding significantly simpler solutions that are no less accurate.

Connected through hyperedge HE-1 (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-2 (induced by source paper "ipboost non convex boosting via integer programming"):
  with members:
    - "evaluating robustness of neural networks with mixed integer programming"
    - "optimal generalized decision trees via integer programming"
    - "minimizing the maximal loss how and why"

Connected through hyperedge HE-3 (induced by source paper "learning fair rule lists"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "the bouncer problem challenges to remote explainability"
    - "fairwashing the risk of rationalization"
    - "the dangers of post hoc interpretability unjustified counterfactual explanations"
    - "learning optimal and fair decision trees for non discriminative decision making"
    - "faht an adaptive fairness aware decision tree classifier"
    - "how can we fool lime and shap adversarial attacks on post hoc explanation methods"
    - "fooling neural network interpretations via adversarial model manipulation"

Connected through hyperedge HE-4 (induced by source paper "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "generalized linear rule models"
    - "generating contrastive explanations with monotonic attribute functions"
    - "improving simple models with confidence profiles"
    - "ted teaching ai to explain its decisions"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "explanation in artificial intelligence insights from the social sciences"

Connected through hyperedge HE-5 (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"
    - "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"
    - "accountability of ai under the law the role of explanation"

Connected through hyperedge HE-6 (induced by source paper "gaining free or low cost transparency with interpretable partial substitute"):
  with members:
    - "hybrid predictive model when an interpretable model collaborates with a black box model"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "interpretable explorable approximations of black box models"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "xgboost a scalable tree boosting system"
    - "scalable bayesian rule lists"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-7 (induced by source paper "characterization of overlap in observational studies"):
  with members:
    - "off policy deep reinforcement learning without exploration"
    - "scalable bayesian rule lists"
    - "falling rule lists"
    - "fairness through awareness"

Connected through hyperedge HE-8 (induced by source paper "libre learning interpretable boolean rule ensembles"):
  with members:
    - "an optimization approach to learning falling rule lists"
    - "explanation in artificial intelligence insights from the social sciences"
    - "scalable bayesian rule lists"

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)