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 "3d scene parsing via class wise adaptation"):
  Abstract: We propose the method that uses only computer graphics datasets to parse the real world 3D scenes. 3D scene parsing based on semantic segmentation is required to implement the categorical interaction in the virtual world. Convolutional Neural Networks (CNNs) have recently shown state-of-theart performance on computer vision tasks including semantic segmentation. However, collecting and annotating a huge amount of data are needed to train CNNs. Especially in the case of semantic segmentation, annotating pixel by pixel takes a significant amount of time and often makes mistakes. In contrast, computer graphics can generate a lot of accurate annotated data and easily scale up by changing camera positions, textures and lights. Despite these advantages, models trained on computer graphics datasets cannot perform well on real data, which is known as the domain shift. To address this issue, we first present that depth modal and synthetic noise are effective to reduce the domain shift. Then, we develop the class-wise adaptation which obtains domain invariant features of CNNs. To reduce the domain shift, we create computer graphics rooms with a lot of props, and provide photo-realistic rendered images.We also demonstrate the application which is combined semantic segmentation with Simultaneous Localization and Mapping (SLAM). Our application performs accurate 3D scene parsing in real-time on an actual room.

Includes members:
  Member "end to end recovery of human shape and pose":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "adversarial discriminative domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "curriculum domain adaptation for semantic segmentation of urban scenes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "unsupervised domain adaptation for semantic segmentation with gans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised domain adaptation using generative adversarial networks for semantic segmentation of aerial images")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "playing for benchmarks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "virtual kitti 2")
  Member "a review on deep learning techniques applied to semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf")
  Member "unrealcv connecting computer vision to unreal engine":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the synthinel 1 dataset a collection of high resolution synthetic overhead imagery for building segmentation")

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)