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 "sam the sensitivity of attribution methods to hyperparameters"):
  Abstract: Attribution methods can provide powerful insights into the reasons for a classifier's decision. We argue that a key desideratum of an explanation method is its robustness to input hyperparameters which are often randomly set or empirically tuned. High sensitivity to arbitrary hyperparameter choices does not only impede reproducibility but also questions the correctness of an explanation and impairs the trust of end-users. In this paper, we provide a thorough empirical study on the sensitivity of existing attribution methods. We found an alarming trend that many methods are highly sensitive to changes in their common hyperparameters e.g. even changing a random seed can yield a different explanation! Interestingly, such sensitivity is not reflected in the average explanation accuracy scores over the dataset as commonly reported in the literature. In addition, explanations generated for robust classifiers (i.e. which are trained to be invariant to pixel-wise perturbations) are surprisingly more robust than those generated for regular classifiers.

Includes members:
  Member "interpretable and fine grained visual explanations for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the benefits of attributional robustness")
  Member "towards explainable artificial intelligence":
    also connected through other hyperedges:
      (none)
  Member "visualizing deep networks by optimizing with integrated gradients":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explaining convolutional neural networks using softmax gradient layer wise relevance propagation")
  Member "removing input features via a generative model to explain their attributions to classifier s decisions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep url a model aware approach to blind deconvolution based on deep unfolded richardson lucy network")
  Member "methods for interpreting and understanding deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "understanding deep architectures by visual summaries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a tour of convolutional networks guided by linear interpreters")
  Member "interpreting and explaining deep neural networks for classification of audio signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on explainable artificial intelligence xai towards medical xai")
  Member "explaining image classifiers by counterfactual generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "aggregating explanation methods for stable and robust explainability")

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.CV(Computer Vision and Pattern Recognition)