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 "disentangling content and style via unsupervised geometry distillation"):
  Abstract: It is challenging to disentangle an object into two orthogonal spaces of content and style since each can influence the visual observation differently and unpredictably. It is rare for one to have access to a large number of data to help separate the influences. In this paper, we present a novel framework to learn this disentangled representation in a completely unsupervised manner. We address this problem in a two-branch Autoencoder framework. For the structural content branch, we project the latent factor into a soft structured point tensor and constrain it with losses derived from prior knowledge. This constraint encourages the branch to distill geometry information. Another branch learns the complementary style information. The two branches form an effective framework that can disentangle object's content-style representation without any human annotation. We evaluate our approach on four image datasets, on which we demonstrate the superior disentanglement and visual analogy quality both in synthesized and real-world data. We are able to generate photo-realistic images with 256*256 resolution that are clearly disentangled in content and style.

Includes members:
  Member "unsupervised discovery of object landmarks as structural representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency")
  Member "a variational u net for conditional appearance and shape generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "image to image translation for cross domain disentanglement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation")
  Member "unsupervised learning of object frames by dense equivariant image labelling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised disentanglement of pose appearance and background from images and videos")
  Member "end to end weakly supervised semantic alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "os2d one stage one shot object detection by matching anchor features")
  Member "variational inference of disentangled latent concepts from unlabeled observations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "pose guided person image generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chained representation cycling learning to estimate 3d human pose and shape by cycling between representations")
  Member "neural face editing with intrinsic image disentangling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "copy and paste gan face hallucination from shaded thumbnails")

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)