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 "g 3 an this video does not exist disentangling motion and appearance for video generation"):
  Abstract: Creating realistic human videos introduces the challenge of being able to simultaneously generate both appearance, as well as motion. To tackle this challenge, we propose the novel spatio-temporal GAN-architecture $G^3AN$, which seeks to capture the distribution of high dimensional video data and to model appearance and motion in disentangled manner. The latter is achieved by decomposing appearance and motion in a three-stream Generator, where the main stream aims to model spatio-temporal consistency, whereas the two auxiliary streams augment the main stream with multi-scale appearance and motion features, respectively. An extensive quantitative and qualitative analysis shows that our model systematically and significantly outperforms state-of-the-art methods on the facial expression datasets MUG and UvA-NEMO, as well as the Weizmann and UCF101 datasets on human action. Additional analysis on the learned latent representations confirms the successful decomposition of appearance and motion.

Includes members:
  Member "video generation from single semantic label map":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "visionnet a drivable space based interactive motion prediction network for autonomous driving")
  Member "guided image to image translation with bi directional feature transformation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "pose guided human video generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial video generation on complex datasets")
  Member "learning to forecast and refine residual motion for image to video generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "first order motion model for image animation")
  Member "smit stochastic multi label image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stargan v2 diverse image synthesis for multiple domains")
  Member "video prediction with appearance and motion conditions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object centric image generation from layouts")
  Member "flow grounded spatial temporal video prediction from still images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformation based adversarial video prediction on large scale data")

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)