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 "do explanations reflect decisions a machine centric strategy to quantify the performance of explainability algorithms"):
  Abstract: There has been a significant surge of interest recently around the concept of explainable artificial intelligence (XAI), where the goal is to produce an interpretation for a decision made by a machine learning algorithm. Of particular interest is the interpretation of how deep neural networks make decisions, given the complexity and `black box' nature of such networks. Given the infancy of the field, there has been very limited exploration into the assessment of the performance of explainability methods, with most evaluations centered around subjective visual interpretation of the produced interpretations. In this study, we explore a more machine-centric strategy for quantifying the performance of explainability methods on deep neural networks via the notion of decision-making impact analysis. We introduce two quantitative performance metrics: i) Impact Score, which assesses the percentage of critical factors with either strong confidence reduction impact or decision changing impact, and ii) Impact Coverage, which assesses the percentage coverage of adversarially impacted factors in the input. A comprehensive analysis using this approach was conducted on several state-of-the-art explainability methods (LIME, SHAP, Expected Gradients, GSInquire) on a ResNet-50 deep convolutional neural network using a subset of ImageNet for the task of image classification. Experimental results show that the critical regions identified by LIME within the tested images had the lowest impact on the decision-making process of the network (~38%), with progressive increase in decision-making impact for SHAP (~44%), Expected Gradients (~51%), and GSInquire (~76%). While by no means perfect, the hope is that the proposed machine-centric strategy helps push the conversation forward towards better metrics for evaluating explainability methods and improve trust in deep neural networks.

Includes members:
  Member "human vs machine attention in neural networks a comparative study":
    also connected through other hyperedges:
      (none)
  Member "learning explainable models using attribution priors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks")
  Member "a survey on explainable artificial intelligence xai towards medical xai":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural generators of sparse local linear models for achieving both accuracy and interpretability")
  Member "probing neural network comprehension of natural language arguments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reclor a reading comprehension dataset requiring logical reasoning")
  Member "adversarial patch":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems")
  Member "allennlp a deep semantic natural language processing platform":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension")
  Member "ferminets learning generative machines to generate efficient neural networks via generative synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attonets compact and efficient deep neural networks for the edge via human machine collaborative design")
  Member "threat of adversarial attacks on deep learning in computer vision a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")

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)