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 "learning from simulated and unsupervised images through adversarial training"):
  Abstract: With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabeled real data, while preserving the annotation information from the simulator. We develop a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. We make several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts, and stabilize training: (i) a 'self-regularization' term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images. We show that this enables generation of highly realistic images, which we demonstrate both qualitatively and with a user study. We quantitatively evaluate the generated images by training models for gaze estimation and hand pose estimation. We show a significant improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data.

Includes members:
  Member "unrealcv connecting computer vision to unreal engine":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the synthinel 1 dataset a collection of high resolution synthetic overhead imagery for building segmentation")
  Member "stacked hourglass networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "precomputed real time texture synthesis with markovian generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "seqgan sequence generative adversarial nets with policy gradient":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "pixel level domain transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "show attend and translate unsupervised image translation with self regularization and attention")
  Member "mocap guided data augmentation for 3d pose estimation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "virtual worlds as proxy for multi object tracking analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation")
  Member "generating images with recurrent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "synthetic examples improve generalization for rare classes")

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)