human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning global transparent models from local contrastive explanations"):
  Abstract: There is a rich and growing literature on producing local point wise contrastive/counterfactual explanations for complex models. These methods highlight what is important to justify the classification and/or produce a contrast point that alters the final classification. Other works try to build globally interpretable models like decision trees and rule lists directly by efficient model search using the data or by transferring information from a complex model using distillation-like methods. Although these interpretable global models can be useful, they may not be consistent with local explanations from a specific complex model of choice. In this work, we explore the question: Can we produce a transparent global model that is consistent with/derivable from local explanations? Based on a key insight we provide a novel method where every local contrastive/counterfactual explanation can be turned into a Boolean feature. These Boolean features are sparse conjunctions of binarized features. The dataset thus constructed is consistent with local explanations by design and one can train an interpretable model like a decision tree on it. We note that this approach strictly loses information due to reliance only on sparse local explanations, nonetheless, we demonstrate empirically that in many cases it can still be competitive with respect to the complex model's performance and also other methods that learn directly from the original dataset. Our approach also provides an avenue to benchmark local explanation methods in a quantitative manner.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "explaining machine learning classifiers through diverse counterfactual explanations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "model agnostic contrastive explanations for structured data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explaining groups of points in low dimensional representations")
  Member "improving simple models with confidence profiles":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainable machine learning in deployment")
  Member "explanations based on the missing towards contrastive explanations with pertinent negatives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences")
  Member "a unified approach to interpreting model predictions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "interpreting blackbox models via model extraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "right for the wrong scientific reasons revising deep networks by interacting with their explanations")
  Member "generating visual explanations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "explaining with counter visual attributes and examples")

Question: Please tell me which class the source paper 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)