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 "gesturegan for hand gesture to gesture translation in the wild"):
  Abstract: Hand gesture-to-gesture translation in the wild is a challenging task since hand gestures can have arbitrary poses, sizes, locations and self-occlusions. Therefore, this task requires a high-level understanding of the mapping between the input source gesture and the output target gesture. To tackle this problem, we propose a novel hand Gesture Generative Adversarial Network (GestureGAN). GestureGAN consists of a single generator $G$ and a discriminator $D$, which takes as input a conditional hand image and a target hand skeleton image. GestureGAN utilizes the hand skeleton information explicitly, and learns the gesture-to-gesture mapping through two novel losses, the color loss and the cycle-consistency loss. The proposed color loss handles the issue of "channel pollution" while back-propagating the gradients. In addition, we present the Fr\'echet ResNet Distance (FRD) to evaluate the quality of generated images. Extensive experiments on two widely used benchmark datasets demonstrate that the proposed GestureGAN achieves state-of-the-art performance on the unconstrained hand gesture-to-gesture translation task. Meanwhile, the generated images are in high-quality and are photo-realistic, allowing them to be used as data augmentation to improve the performance of a hand gesture classifier. Our model and code are available at this https URL

Includes members:
  Member "every smile is unique landmark guided diverse smile generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "first order motion model for image animation")
  Member "face aging with contextual generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ugan untraceable gan for multi domain face translation")
  Member "gp gan gender preserving gan for synthesizing faces from landmarks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "geometry contrastive gan for facial expression transfer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cascade ef gan progressive facial expression editing with local focuses")
  Member "cross domain human parsing via adversarial feature and label adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ugan untraceable gan for multi domain face translation")
  Member "midinet a convolutional generative adversarial network for symbolic domain music generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "music generation by deep learning challenges and directions")
  Member "dual discriminator generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "microbatchgan stimulating diversity with multi adversarial discrimination")
  Member "transformation grounded image generation network for novel 3d view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled 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)