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 "meshdepth disconnected mesh based deep depth prediction"):
  Abstract: We propose a novel method for mesh-based single-view depth estimation using Convolutional Neural Networks (CNNs). Conventional CNN-based methods are only suitable for representing simple 3D objects because they estimate the deformation from a predefined simple mesh such as a cube or sphere. As a 3D scene representation, we introduce a disconnected mesh made of 2D mesh adaptively determined on the input image. We made a CNN-based framework to compute depths and normals of faces of the mesh. Because of the representation, our method can handle complex indoor scenes. Using common RGBD datasets, we show that our model achieved best or comparable performance comparing to the state-of-the-art pixel-wise dense methods. It should be noted that our method significantly reduces the number of the parameter representing the 3D structure.

Includes members:
  Member "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "seqxy2seqz structure learning for 3d shapes by sequentially predicting 1d occupancy segments from 2d coordinates")
  Member "deeptam deep tracking and mapping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "sfmlearner learning monocular depth ego motion using meaningful geometric constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised deep pose corrections for robust visual odometry")
  Member "efficient semantic image segmentation with superpixel pooling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "occuseg occupancy aware 3d instance segmentation")
  Member "lego learning edge with geometry all at once by watching videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields")
  Member "digging into self supervised monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation")
  Member "mvsnet depth inference for unstructured multi view stereo":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "sparse to dense depth prediction from sparse depth samples and a single image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "depth estimation by learning triangulation and densification of sparse points for multi view stereo")

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)