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 contrastive explanations for structured data"
  Abstract: Recently, a method [7] was proposed to generate contrastive explanations for differentiable models such as deep neural networks, where one has complete access to the model. In this work, we propose a method, Model Agnostic Contrastive Explanations Method (MACEM), to generate contrastive explanations for \emph{any} classification model where one is able to \emph{only} query the class probabilities for a desired input. This allows us to generate contrastive explanations for not only neural networks, but models such as random forests, boosted trees and even arbitrary ensembles that are still amongst the state-of-the-art when learning on structured data [13]. Moreover, to obtain meaningful explanations we propose a principled approach to handle real and categorical features leading to novel formulations for computing pertinent positives and negatives that form the essence of a contrastive explanation. A detailed treatment of the different data types of this nature was not performed in the previous work, which assumed all features to be positive real valued with zero being indicative of the least interesting value. We part with this strong implicit assumption and generalize these methods so as to be applicable across a much wider range of problem settings. We quantitatively and qualitatively validate our approach over 5 public datasets covering diverse domains.

Connected through hyperedge HE-1 (induced by source paper "explaining groups of points in low dimensional representations"):
  with members:
    - "from clustering to cluster explanations via neural networks"
    - "counterfactual visual explanations"
    - "model agnostic supervised local 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-2 (induced by source paper "learning global transparent models from local contrastive explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "improving simple models with confidence profiles"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "a unified approach to interpreting model predictions"
    - "interpreting blackbox models via model extraction"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"

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 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"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"

Connected through hyperedge HE-4 (induced by source paper "an explanation method for siamese neural networks"):
  with members:
    - "evaluating explainers via perturbation"
    - "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"
    - "alime autoencoder based approach for local interpretability"
    - "dlime a deterministic local interpretable model agnostic explanations approach for computer aided diagnosis systems"
    - "interpretable counterfactual explanations guided by prototypes"
    - "normlime a new feature importance metric for explaining deep neural networks"
    - "a survey of evaluation methods and measures for interpretable machine learning"
    - "metric learning for novelty and anomaly detection"

Connected through hyperedge HE-5 (induced by source paper "interpretable counterfactual explanations guided by prototypes"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "kernel stein tests for multiple model comparison"
    - "on the accuracy of influence functions for measuring group effects"
    - "generating contrastive explanations with monotonic attribute functions"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "tip typifying the interpretability of procedures"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "progressive growing of gans for improved quality stability and variation"

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)