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 pose from appearance in monochrome hand images"):
  Abstract: Hand pose estimation from the monocular 2D image is challenging due to the variation in lighting, appearance, and background. While some success has been achieved using deep neural networks, they typically require collecting a large dataset that adequately samples all the axes of variation of hand images. It would, therefore, be useful to find a representation of hand pose which is independent of the image appearance~(like hand texture, lighting, background), so that we can synthesize unseen images by mixing pose-appearance combinations. In this paper, we present a novel technique that disentangles the representation of pose from a complementary appearance factor in 2D monochrome images. We supervise this disentanglement process using a network that learns to generate images of hand using specified pose+appearance features. Unlike previous work, we do not require image pairs with a matching pose; instead, we use the pose annotations already available and introduce a novel use of cycle consistency to ensure orthogonality between the factors. Experimental results show that our self-disentanglement scheme successfully decomposes the hand image into the pose and its complementary appearance features of comparable quality as the method using paired data. Additionally, training the model with extra synthesized images with unseen hand-appearance combinations by re-mixing pose and appearance factors from different images can improve the 2D pose estimation performance.

Includes members:
  Member "synthesizing images of humans in unseen poses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "disentangling latent hands for image synthesis and pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d hand pose estimation in the wild via graph refinement under adversarial learning")
  Member "augmented skeleton space transfer for depth based hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "exploring disentangled feature representation beyond face identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial disentanglement with grouped observations")
  Member "image to image translation for cross domain disentanglement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation")
  Member "vip cnn visual phrase guided convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "variational inference of disentangled latent concepts from unlabeled observations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")

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)