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 "shapecodes self supervised feature learning by lifting views to viewgrids"):
  Abstract: We introduce an unsupervised feature learning approach that embeds 3D shape information into a single-view image representation. The main idea is a self-supervised training objective that, given only a single 2D image, requires all unseen views of the object to be predictable from learned features. We implement this idea as an encoder-decoder convolutional neural network. The network maps an input image of an unknown category and unknown viewpoint to a latent space, from which a deconvolutional decoder can best "lift" the image to its complete viewgrid showing the object from all viewing angles. Our class-agnostic training procedure encourages the representation to capture fundamental shape primitives and semantic regularities in a data-driven manner---without manual semantic labels. Our results on two widely-used shape datasets show 1) our approach successfully learns to perform "mental rotation" even for objects unseen during training, and 2) the learned latent space is a powerful representation for object recognition, outperforming several existing unsupervised feature learning methods.

Includes members:
  Member "learning pose specific representations by predicting different views":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dual grid net hand mesh vertex regression from single depth maps")
  Member "deep view morphing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint wasserstein distribution matching")
  Member "transitive invariance for self supervised visual representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "im2pano3d extrapolating 360 structure and semantics beyond the field of view":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "emergence of exploratory look around behaviors through active observation completion")
  Member "colorization as a proxy task for visual understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rgb based semantic segmentation using self supervised depth pre training")
  Member "learning to look around intelligently exploring unseen environments for unknown tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an exploration of embodied visual exploration")
  Member "look ahead before you leap end to end active recognition by forecasting the effect of motion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "emergence of exploratory look around behaviors through active observation completion")
  Member "3d r2n2 a unified approach for single and multi view 3d object reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")

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)