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 "boosting occluded image classification via subspace decomposition based estimation of deep features"):
  Abstract: Classification of partially occluded images is a highly challenging computer vision problem even for the cutting-edge deep learning technologies. To achieve a robust image classification for occluded images, this article proposes a novel scheme using the subspace decomposition-based estimation (SDBE). The proposed SDBE-based classification scheme first employs a base convolutional neural network to extract the deep feature vector (DFV) and then utilizes the SDBE to compute the DFV of the original occlusion-free image for classification. The SDBE is performed by projecting the DFV of the occluded image onto the linear span of a class dictionary (CD) along the linear span of an occlusion error dictionary (OED). The CD and OED are constructed, respectively, by concatenating the DFVs of a training set and the occlusion error vectors of an extra set of image pairs. Two implementations of the SDBE are studied in this article: 1) the l₁-norm and 2) the squared l₂-norm regularized least-squares estimates. By employing the ResNet-152, pretrained on the ImageNet Large-Scale Visual Recognition Challenge 2012 (ILSVRC2012) training set, as the base network, the proposed SBDE-based classification scheme is extensively evaluated on the Caltech-101 and ILSVRC2012 datasets. Extensive experimental results demonstrate that the proposed SDBE-based scheme dramatically boosts the classification accuracy for occluded images, and achieves around 22.25% increase in classification accuracy under 20% occlusion on the ILSVRC2012 dataset.

Includes members:
  Member "adversarially approximated autoencoder for image generation and manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "facelet bank for fast portrait manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "one shot face reenactment")
  Member "mdcn multi scale deep inception convolutional neural networks for efficient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "learning depth from single images with deep neural network embedding focal length":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "generative image inpainting with contextual attention":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "network dissection quantifying interpretability of deep visual representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "cloud based deep learning of big eeg data for epileptic seizure prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers")
  Member "perceptual generative adversarial networks for small object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "extended feature pyramid network for small object detection")

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)