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 "a large rgb d dataset for semi supervised monocular depth estimation"):
  Abstract: The recent advance of monocular depth estimation is largely based on deeply nested convolutional networks, combined with supervised training. However, it still remains arduous to collect large-scale ground truth depth (or disparity) maps for supervising the networks. This paper presents a simple yet effective semi-supervised approach for monocular depth estimation. Inspired by the human visual system, we propose a student-teacher strategy in which a shallow student network is trained with the auxiliary information obtained from a deeper and accurate teacher network. Specifically, we first train the stereo teacher network fully utilizing the binocular perception of 3D geometry, and then use depth predictions of the teacher network for supervising the student network for monocular depth inference. This enables us to exploit all available depth data from massive unlabeled stereo pairs that are relatively easier-to-obtain. We further introduce a data ensemble strategy that merges multiple depth predictions of the teacher network to improve the training samples for the student network. Additionally, stereo confidence maps are provided to avoid inaccurate depth estimates being used when supervising the student network. Our new training data, consisting of 1 million outdoor stereo images taken using hand-held stereo cameras, is hosted at the project webpage. Lastly, we demonstrate that the monocular depth estimation network provides feature representations that are suitable for some high-level vision tasks such as semantic segmentation and road detection. Extensive experiments demonstrate the effectiveness and flexibility of the proposed method in various outdoor scenarios.

Includes members:
  Member "dense 3d object reconstruction from a single depth view":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multimodal shape completion via conditional generative adversarial networks")
  Member "colorization as a proxy task for visual understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rgb based semantic segmentation using self supervised depth pre training")
  Member "data distillation towards omni supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "few shot few shot learning and the role of spatial attention")
  Member "cascade residual learning a two stage convolutional neural network for stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep depth prior for multi view stereo")
  Member "semi supervised deep learning for monocular depth map prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "the cityscapes dataset for semantic urban scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "unsupervised monocular depth estimation with left right consistency":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")
  Member "multinet real time joint semantic reasoning for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "self supervised learning for autonomous vehicles perception a conciliation between analytical and learning methods")

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)