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 "gan compression efficient architectures for interactive conditional gans"):
  Abstract: Conditional Generative Adversarial Networks (cGANs) have enabled controllable image synthesis for many computer vision and graphics applications. However, recent cGANs are 1-2 orders of magnitude more computationally-intensive than modern recognition CNNs. For example, GauGAN consumes 281G MACs per image, compared to 0.44G MACs for MobileNet-v3, making it difficult for interactive deployment. In this work, we propose a general-purpose compression framework for reducing the inference time and model size of the generator in cGANs. Directly applying existing CNNs compression methods yields poor performance due to the difficulty of GAN training and the differences in generator architectures. We address these challenges in two ways. First, to stabilize the GAN training, we transfer knowledge of multiple intermediate representations of the original model to its compressed model, and unify unpaired and paired learning. Second, instead of reusing existing CNN designs, our method automatically finds efficient architectures via neural architecture search (NAS). To accelerate the search process, we decouple the model training and architecture search via weight sharing. Experiments demonstrate the effectiveness of our method across different supervision settings (paired and unpaired), model architectures, and learning methods (e.g., pix2pix, GauGAN, CycleGAN). Without losing image quality, we reduce the computation of CycleGAN by more than 20X and GauGAN by 9X, paving the way for interactive image synthesis. The code and demo are publicly available.

Includes members:
  Member "learning character agnostic motion for motion retargeting in 2d":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints")
  Member "design automation for efficient deep learning computing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "previous a methodology for prediction of visual inference performance on iot devices")
  Member "searching for mobilenetv3":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "searching for winograd aware quantized networks")
  Member "single path one shot neural architecture search with uniform sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixpath a unified approach for one shot neural architecture search")
  Member "semantic image synthesis with spatially adaptive normalization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object centric image generation from layouts")
  Member "co evolutionary compression for unpaired image translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cars continuous evolution for efficient neural architecture search")
  Member "compressing gans using knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stylegan2 distillation for feed forward image manipulation")
  Member "video to video synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stochastic latent residual video prediction")

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)