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 "attentiongan unpaired image to image translation using attention guided generative adversarial networks"):
  Abstract: State-of-the-art methods in the unpaired image-to-image translation are capable of learning a mapping from a source domain to a target domain with unpaired image data. Though the existing methods have achieved promising results, they still produce unsatisfied artifacts, being able to convert low-level information while limited in transforming high-level semantics of input images. One possible reason is that generators do not have the ability to perceive the most discriminative semantic parts between the source and target domains, thus making the generated images low quality. In this paper, we propose a new Attention-Guided Generative Adversarial Networks (AttentionGAN) for the unpaired image-to-image translation task. AttentionGAN can identify the most discriminative semantic objects and minimize changes of unwanted parts for semantic manipulation problems without using extra data and models. The attention-guided generators in AttentionGAN are able to produce attention masks via a built-in attention mechanism, and then fuse the generation output with the attention masks to obtain high-quality target images. Accordingly, we also design a novel attention-guided discriminator which only considers attended regions. Extensive experiments are conducted on several generative tasks, demonstrating that the proposed model is effective to generate sharper and more realistic images compared with existing competitive models. The source code for the proposed AttentionGAN is available at this https URL.

Includes members:
  Member "show attend and translate unsupervised image translation with self regularization and attention":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disentangled makeup transfer with generative adversarial network")
  Member "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "multi channel attention selection gan with cascaded semantic guidance for cross view image translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "attention guided generative adversarial networks for unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cycle in cycle generative adversarial networks for keypoint guided image generation")
  Member "dual generator generative adversarial networks for multi domain image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "gesturegan for hand gesture to gesture translation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "mix and match networks encoder decoder alignment for zero pair image translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sdit scalable and diverse cross domain image translation")
  Member "multimodal unsupervised image to image translation":
    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)