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 "localizing multi scale semantic patches for image classification"):
  Abstract: Deep convolutional neural networks (CNN) always non-linearly aggregate the information from the whole input image, which results in the difficult to interpret how relevant regions contribute the final prediction. In this paper, we construct a light-weight AnchorNet combined with our proposed algorithms to localize multi-scale semantic patches, where the contribution of each patch can be determined due to the linearly spatial aggregation before the softmax layer. Visual explanation shows that localized patches can indeed retain the semantics of the original images, while helping us to further analyze the feature extraction of localization branches with various receptive fields. For more practical, we use localized patches for downstream classification tasks across widely applied networks. Experimental results demonstrate that replacing the original images can get a clear inference acceleration with only tiny performance degradation.

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 "saccader improving accuracy of hard attention models for vision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "concept whitening for interpretable image recognition")
  Member "metapruning meta learning for automatic neural network channel pruning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "domain aware dynamic networks")
  Member "approximating cnns with bag of local features models works surprisingly well on imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning representations by predicting bags of visual words")
  Member "searching for mobilenetv3":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "gated convolutional networks with hybrid connectivity for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "drnet dissect and reconstruct the convolutional neural network via interpretable manners")
  Member "attention branch network learning of attention mechanism for visual explanation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting spatial invariance with low rank local connectivity")
  Member "cbam convolutional block attention module":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)