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 "doc deep occlusion recovering from a single image"):
  Abstract: Recovering the occlusion relationship between stuffs and things is a fundamental ability of human vision, and yields information about the 3D world from 2D images. In this paper, we propose deep occlusion (DOC), which is an end-to-end deep learning strategy that recovers the occlusion boundaries from a single image. Firstly, for each pixel along the boundaries, we introduce an orientation variable, indicating the occlusion relationship between two adjacent regions. Then, deep features, long range context and object-level knowledge are exploited by an end-to-end deep convolutional neural network (DCNN) to accurately locate the boundaries and recover the occlusion relationships. To evaluate and train the DOC network, we construct a large-scale instance occlusion boundary dataset from the PASCAL images, which we call the PASCAL instance occlusion dataset. It includes 20k images (100$\times$ bigger than the existing occlusion datasets for outdoor images) and so is big enough to train and evaluate DCNNs for occlusion learning. Finally, extensive experiments are performed to demonstrate that our approach has strong generalization ability and outperform state-of-the-art strategies.

Includes members:
  Member "holistically nested edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  Member "proposal free network for instance level object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deeperlab single shot image parser")
  Member "joint object and part segmentation using deep learned potentials":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning compositional neural information fusion for human parsing")
  Member "parsenet looking wider to see better":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "understanding deep image representations by inverting them":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crl class representative learning for image classification")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "semantic image segmentation with deep convolutional nets and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)