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 "learning multi scale features for foreground segmentation"):
  Abstract: Foreground segmentation algorithms aim at segmenting moving objects from the background in a robust way under various challenging scenarios. Encoder–decoder-type deep neural networks that are used in this domain recently perform impressive segmentation results. In this work, we propose a variation of our formerly proposed method (Anonymous 2018) that can be trained end-to-end using only a few training examples. The proposed method extends the feature pooling module of FgSegNet by introducing fusion of features inside this module, which is capable of extracting multi-scale features within images, resulting in a robust feature pooling against camera motion, which can alleviate the need of multi-scale inputs to the network. Sample visualizations highlight the regions in the images on which the model is specially focused. It can be seen that these regions are also the most semantically relevant. Our method outperforms all existing state-of-the-art methods in CDnet2014 datasets by an average overall F-measure of 0.9847. We also evaluate the effectiveness of our method on SBI2015 and UCSD Background Subtraction datasets. The source code of the proposed method is made available at https://github.com/lim-anggun/FgSegNet_v2.

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 "foreground segmentation using convolutional neural networks for multiscale feature encoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "how far can you get by combining change detection algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bsuv net a fully convolutional neural network for background subtraction of unseen videos")
  Member "a deep convolutional neural network for background subtraction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "rethinking atrous convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "instance normalization the missing ingredient for fast stylization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

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)