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 "improving feature attribution through input specific network pruning"):
  Abstract: Attributing the output of a neural network to the contribution of given input elements is a way of shedding light on the black-box nature of neural networks. Due to the complexity of current network architectures, current gradient-based attribution methods provide very noisy or coarse results. We propose to prune a neural network for a given single input to keep only neurons that highly contribute to the prediction. We show that by input-specific pruning, network gradients change from reflecting local (noisy) importance information to global importance. Our proposed method is efficient and generates fine-grained attribution maps. We further provide a theoretical justification of the pruning approach relating it to perturbations and validate it through a novel experimental setup. Our method is evaluated by multiple benchmarks: sanity checks, pixel perturbation, and Remove-and-Retrain (ROAR). These benchmarks evaluate the method from different perspectives and our method performs better than other methods across all evaluations.

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 "full gradient representation for neural network visualization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "measuring and improving the quality of visual explanations")
  Member "explaining deep neural networks with a polynomial time algorithm for shapley values approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fedcoin a peer to peer payment system for federated learning")
  Member "understanding deep networks via extremal perturbations and smooth masks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analysis of explainers of black box deep neural networks for computer vision a survey")
  Member "interpretable and fine grained visual explanations for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters")
  Member "a benchmark for interpretability methods in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "overinterpretation reveals image classification model pathologies")
  Member "visualizing deep networks by optimizing with integrated gradients":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters")
  Member "why are saliency maps noisy cause of and solution to noisy saliency maps":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "when explanations lie why many modified bp attributions fail")

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)