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: "interpretable credit application predictions with counterfactual explanations"
  Abstract: We predict credit applications with off-the-shelf, interchangeable black-box clas-sifiers and we explain single predictions with counterfactual explanations. Coun-terfactual explanations expose the minimal changes required on the input data to obtain a different result e.g., approved vs rejected application. Despite their effectiveness , counterfactuals are mainly designed for changing an undesired outcome of a prediction i.e. loan rejected. Counterfactuals, however, can be difficult to interpret , especially when a high number of features are involved in the explanation. Our contribution is twofold: i) we propose positive counterfactuals, i.e. we adapt counterfactual explanations to also explain accepted loan applications, and ii) we propose two weighting strategies to generate more interpretable counterfactuals. Experiments on the HELOC loan applications dataset show that our contribution outperforms the baseline counterfactual generation strategy, by leading to smaller and hence more interpretable counterfactuals.

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 "the hidden assumptions behind counterfactual explanations and principal reasons"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "efficient search for diverse coherent explanations"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "delayed impact of fair machine learning"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "actionable interpretability through optimizable counterfactual explanations for tree ensembles"):
  with members:
    - "explainable reinforcement learning through a causal lens"
    - "efficient search for diverse coherent explanations"
    - "model agnostic contrastive explanations for structured data"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "generating counterfactual and contrastive explanations using shap"
    - "local rule based explanations of black box decision systems"
    - "deep neural decision trees"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"

Connected through hyperedge HE-4 (induced by source paper "issues with post hoc counterfactual explanations a discussion"):
  with members:
    - "efficient search for diverse coherent explanations"
    - "local rule based explanations of black box decision systems"
    - "defining locality for surrogates in post hoc interpretablity"
    - "smoothgrad removing noise by adding noise"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "accountability of ai under the law the role of explanation"
    - "generalized inverse classification"

Connected through hyperedge HE-5 (induced by source paper "towards realistic individual recourse and actionable explanations in black box decision making systems"):
  with members:
    - "handling incomplete heterogeneous data using vaes"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "adversarial feature learning"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-6 (induced by source paper "towards user empowerment"):
  with members:
    - "issues with post hoc counterfactual explanations a discussion"
    - "handling incomplete heterogeneous data using vaes"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "generalized inverse classification"

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)