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: "model agnostic supervised local explanations"
  Abstract: Model interpretability is an increasingly important component of practical machine learning. Some of the most common forms of interpretability systems are example-based, local, and global explanations. One of the main challenges in interpretability is designing explanation systems that can capture aspects of each of these explanation types, in order to develop a more thorough understanding of the model. We address this challenge in a novel model called MAPLE that uses local linear modeling techniques along with a dual interpretation of random forests (both as a supervised neighborhood approach and as a feature selection method). MAPLE has two fundamental advantages over existing interpretability systems. First, while it is effective as a black-box explanation system, MAPLE itself is a highly accurate predictive model that provides faithful self explanations, and thus sidesteps the typical accuracy-interpretability trade-off. Specifically, we demonstrate, on several UCI datasets, that MAPLE is at least as accurate as random forests and that it produces more faithful local explanations than LIME, a popular interpretability system. Second, MAPLE provides both example-based and local explanations and can detect global patterns, which allows it to diagnose limitations in its local explanations.

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 "explaining groups of points in low dimensional representations"):
  with members:
    - "from clustering to cluster explanations via neural networks"
    - "model agnostic contrastive explanations for structured data"
    - "counterfactual visual explanations"
    - "interpreting neural network judgments via minimal stable and symbolic corrections"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "the mythos of model interpretability"

Connected through hyperedge HE-3 (induced by source paper "model agnostic multilevel explanations"):
  with members:
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "methods for interpreting and understanding deep neural networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "why should i trust you explaining the predictions of any classifier"
    - "falling rule lists"

Connected through hyperedge HE-4 (induced by source paper "on the in fidelity and sensitivity for explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "certified adversarial robustness via randomized smoothing"
    - "towards robust locally linear deep networks"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "counterfactual visual explanations"
    - "interpreting black box predictions using fisher kernels"
    - "representer point selection for explaining deep neural networks"
    - "interpretable deep learning under fire"

Connected through hyperedge HE-5 (induced by source paper "can i trust the explainer verifying post hoc explanatory methods"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "annotation artifacts in natural language inference data"
    - "evaluating neural network explanation methods using hybrid documents and morphological prediction"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "what made you do this understanding black box decisions with sufficient input subsets"
    - "textual explanations for self driving vehicles"
    - "e snli natural language inference with natural language explanations"
    - "defining locality for surrogates in post hoc interpretablity"

Connected through hyperedge HE-6 (induced by source paper "rl lim reinforcement learning based locally interpretable modeling"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "regularizing black box models for improved interpretability"
    - "explaining explanations an overview of interpretability of machine learning"
    - "learning to reweight examples for robust deep learning"
    - "on the robustness of interpretability methods"
    - "online and linear time attention by enforcing monotonic alignments"
    - "interpretable explorable approximations of black box models"
    - "sentence simplification with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "regularizing black box models for improved interpretability"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "functional transparency for structured data a game theoretic approach"
    - "sanity checks for saliency maps"
    - "on the robustness of interpretability methods"
    - "contextual explanation networks"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-8 (induced by source paper "regularizing black box models for improved interpretability hill 2019 version"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "functional transparency for structured data a game theoretic approach"
    - "sanity checks for saliency maps"
    - "on the robustness of interpretability methods"
    - "contextual explanation networks"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"

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)