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 "logan unpaired shape transform in latent overcomplete space"):
  Abstract: We present LOGAN, a deep neural network aimed at learning generic shape transforms from unpaired domains. The network is trained on two sets of shapes, e.g., tables and chairs, but there is neither a pairing between shapes in the two domains to supervise the shape translation nor any point-wise correspondence between any shapes. Once trained, LOGAN takes a shape from one domain and transforms it into the other. Our network consists of an autoencoder to encode shapes from the two input domains into a common latent space, where the latent codes encode multi-scale shape features in an overcomplete manner. The translator is based on a generative adversarial network (GAN), operating in the latent space, where an adversarial loss enforces cross-domain translation while a feature preservation loss ensures that the right shape features are preserved for a natural shape transform. We conduct various ablation studies to validate each of our key network designs and demonstrate superior capabilities in unpaired shape transforms on a variety of examples over baselines and state-of-the-art approaches. We show that our network is able to learn what shape features to preserve during shape transforms, either local or non-local, whether content or style, etc., depending solely on the input domain pairs.

Includes members:
  Member "emerging disentanglement in auto encoder based unsupervised image content transfer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "augmented cyclegan learning many to many mappings from unpaired data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation")
  Member "p2p net bidirectional point displacement net for shape transform":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multimodal shape completion via conditional generative adversarial networks")
  Member "deepmimic example guided deep reinforcement learning of physics based character skills":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "artificial intelligence for prosthetics challenge solutions")
  Member "point convolutional neural networks by extension operators":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pointasnl robust point clouds processing using nonlocal neural networks with adaptive sampling")
  Member "multimodal unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "nam non adversarial unsupervised domain mapping":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mimicgan robust projection onto image manifolds with corruption mimicking")
  Member "pcpnet learning local shape properties from raw point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pointasnl robust point clouds processing using nonlocal neural networks with adaptive sampling")

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)