human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "towards instance level image to image translation"
  Abstract: Unpaired Image-to-image Translation is a new rising and challenging vision problem that aims to learn a mapping between unaligned image pairs in diverse domains. Recent advances in this field like MUNIT and DRIT mainly focus on disentangling content and style/attribute from a given image first, then directly adopting the global style to guide the model to synthesize new domain images. However, this kind of approaches severely incurs contradiction if the target domain images are content-rich with multiple discrepant objects. In this paper, we present a simple yet effective instance-aware image-to-image translation approach (INIT), which employs the fine-grained local (instance) and global styles to the target image spatially. The proposed INIT exhibits three import advantages: (1) the instance-level objective loss can help learn a more accurate reconstruction and incorporate diverse attributes of objects; (2) the styles used for target domain of local/global areas are from corresponding spatial regions in source domain, which intuitively is a more reasonable mapping; (3) the joint training process can benefit both fine and coarse granularity and incorporates instance information to improve the quality of global translation. We also collect a large-scale benchmark for the new instance-level translation task. We observe that our synthetic images can even benefit real-world vision tasks like generic object detection.

Connected through hyperedge HE-1 (induced by source paper "one to one mapping for unpaired image to image translation"):
  with members:
    - "towards learning a self inverse network for bidirectional image to image translation"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "analyzing inverse problems with invertible neural networks"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"
    - "attention gan for object transfiguration in wild images"

Connected through hyperedge HE-2 (induced by source paper "adversarial based knowledge distillation for multi model ensemble and noisy data refinement"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "relational knowledge distillation"
    - "snapshot distillation teacher student optimization in one generation"
    - "knowledge distillation by on the fly native ensemble"
    - "multimodal unsupervised image to image translation"
    - "large scale distributed neural network training through online distillation"
    - "meal multi model ensemble via adversarial learning"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-3 (induced by source paper "domain bridge for unpaired image to image translation and unsupervised domain adaptation"):
  with members:
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "i can see clearly now image restoration via de raining"
    - "physics based rendering for improving robustness to rain"
    - "few shot unsupervised image to image translation"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "adversarial learning and self teaching techniques for domain adaptation in semantic segmentation"
    - "label propagation for deep semi supervised learning"
    - "learning across tasks and domains"

Connected through hyperedge HE-4 (induced by source paper "learning a self inverse network for unpaired bidirectional image to image translation"):
  with members:
    - "towards learning a self inverse network for bidirectional image to image translation"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "harmonic unpaired image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "video to video synthesis"
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "analyzing inverse problems with invertible neural networks"

Connected through hyperedge HE-5 (induced by source paper "towards learning a self inverse network for bidirectional image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "analyzing inverse problems with invertible neural networks"
    - "multimodal unsupervised image to image translation"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "i revnet deep invertible networks"
    - "drit diverse image to image translation via disentangled representations"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-6 (induced by source paper "task aware monocular depth estimation for 3d object detection"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "geometry aware symmetric domain adaptation for monocular depth estimation"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "deep attention based classification network for robust depth prediction"
    - "deep ordinal regression network for monocular depth estimation"

Connected through hyperedge HE-7 (induced by source paper "edit exemplar domain aware image to image translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "relgan multi domain image to image translation via relative attributes"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "multimodal unsupervised image to image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "stereoscopic neural style transfer"
    - "latent filter scaling for multimodal unsupervised image to image translation"
    - "conditional image to image translation"

Connected through hyperedge HE-8 (induced by source paper "region and object based panoptic image synthesis through conditional gans"):
  with members:
    - "panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "recycle gan unsupervised video retargeting"
    - "attention guided unified network for panoptic segmentation"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "instagan instance aware image to image translation"
    - "drit diverse image to image translation via disentangled representations"

Question: Please tell me which class the center node 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)