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: "a survey of methods for explaining black box models"
  Abstract: In the last years many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness sometimes at the cost of scarifying accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, delineating explicitly or implicitly its own definition of interpretability and explanation. The aim of this paper is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.

Connected through hyperedge HE-1 (induced by source paper "vice visual counterfactual explanations for machine learning models"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "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-2 (induced by source paper "fooling neural network interpretations via adversarial model manipulation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial training for free"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "interpretable deep learning under fire"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "rise randomized input sampling for explanation of black box models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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"
    - "interpretable credit application predictions with counterfactual explanations"
    - "local rule based explanations of black box decision systems"
    - "deep neural decision trees"

Connected through hyperedge HE-4 (induced by source paper "responsible ai by design in practice"):
  with members:
    - "explaining reinforcement learning to mere mortals an empirical study"
    - "open the black box data driven explanation of black box decision systems"
    - "ted teaching ai to explain its decisions"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "mitigating unwanted biases with adversarial learning"
    - "explaining explanations an overview of interpretability of machine learning"
    - "artificial intelligence and legal liability"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"

Connected through hyperedge HE-5 (induced by source paper "why does my model fail contrastive local explanations for retail forecasting"):
  with members:
    - "efficient search for diverse coherent explanations"
    - "explainable ai for trees from local explanations to global understanding"
    - "grounding visual explanations"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "finding influential training samples for gradient boosted decision trees"
    - "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-6 (induced by source paper "explaining predictions from tree based boosting ensembles"):
  with members:
    - "on the robustness of interpretability methods"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-7 (induced by source paper "explaining explanations an overview of interpretability of machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "visual interpretability for deep learning a survey"
    - "unsupervised learning of neural networks to explain neural networks"
    - "smoothgrad removing noise by adding noise"
    - "network dissection quantifying interpretability of deep visual representations"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "semantic bottleneck for computer vision tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "interpreting cnns via decision trees"
    - "explanation methods in deep learning users values concerns and challenges"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "network dissection quantifying interpretability of deep visual representations"

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.CY(Computers and Society)