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 "iterative and adaptive sampling with spatial attention for black box model explanations"):
  Abstract: Deep neural networks have achieved great success in many real-world applications, yet it remains unclear and difficult to explain their decision-making process to an end-user. In this paper, we address the explainable AI problem for deep neural networks with our proposed framework, named IASSA, which generates an importance map indicating how salient each pixel is for the model's prediction with an iterative and adaptive sampling module. We employ an affinity matrix calculated on multi-level deep learning features to explore long-range pixel-to-pixel correlation, which can shift the saliency values guided by our long-range and parameter-free spatial attention. Extensive experiments on the MS-COCO dataset show that our proposed approach matches or exceeds the performance of state-of-the-art black-box explanation methods.

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 "faceforensics learning to detect manipulated facial images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "watch your up convolution cnn based generative deep neural networks are failing to reproduce spectral distributions")
  Member "embedding human knowledge in deep neural network via attention map":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interpretations are useful penalizing explanations to align neural networks with prior knowledge")
  Member "why do these match explaining the behavior of image similarity models":
    also connected through other hyperedges:
      (none)
  Member "interpretable and fine grained visual explanations for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters")
  Member "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships")
  Member "argan attentive recurrent generative adversarial network for shadow detection and removal":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards ghost free shadow removal via dual hierarchical aggregation network and shadow matting gan")
  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")

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)