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 "cam convs camera aware multi scale convolutions for single view depth"):
  Abstract: Single-view depth estimation suffers from the problem that a network trained on images from one camera does not generalize to images taken with a different camera model. Thus, changing the camera model requires collecting an entirely new training dataset. In this work, we propose a new type of convolution that can take the camera parameters into account, thus allowing neural networks to learn calibration-aware patterns. Experiments confirm that this improves the generalization capabilities of depth prediction networks considerably, and clearly outperforms the state of the art when the train and test images are acquired with different cameras.

Includes members:
  Member "implicit 3d orientation learning for 6d object detection from rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  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 "deep attention based classification network for robust depth prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from open set to closed set supervised spatial divide and conquer for object counting")
  Member "codeslam learning a compact optimisable representation for dense visual slam":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "just in time reconstruction inpainting sparse maps using single view depth predictors as priors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "depth completion via deep basis fitting")
  Member "deep ordinal regression network for monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "deep depth completion of a single rgb d image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "depth estimation by learning triangulation and densification of sparse points for multi view stereo")
  Member "deepmvs learning multi view stereopsis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dsgn deep stereo geometry network for 3d 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)