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 "interpretable and fine grained visual explanations for convolutional neural networks"):
  Abstract: To verify and validate networks, it is essential to gain insight into their decisions, limitations as well as possible shortcomings of training data. In this work, we propose a post-hoc, optimization based visual explanation method, which highlights the evidence in the input image for a specific prediction. Our approach is based on a novel technique to defend against adversarial evidence (i.e. faulty evidence due to artefacts) by filtering gradients during optimization. The defense does not depend on human-tuned parameters. It enables explanations which are both fine-grained and preserve the characteristics of images, such as edges and colors. The explanations are interpretable, suited for visualizing detailed evidence and can be tested as they are valid model inputs. We qualitatively and quantitatively evaluate our approach on a multitude of models and datasets.

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 "towards explanation of dnn based prediction with guided feature inversion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fda feature disruptive attack")
  Member "visual interpretability for deep learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "regional multi scale approach for visually pleasing explanations of deep neural networks":
    also connected through other hyperedges:
      (none)
  Member "techniques for interpretable machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "local explanation methods for deep neural networks lack sensitivity to parameter values":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tedl a text encryption method based on deep learning")
  Member "a theoretical explanation for perplexing behaviors of backpropagation based visualizations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when explanations lie why many modified bp attributions fail")
  Member "explaining image classifiers by counterfactual generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters")

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)