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: "unsupervised attention guided image to image translation"
  Abstract: Current unsupervised image-to-image translation techniques struggle to focus their attention on individual objects without altering the background or the way multiple objects interact within a scene. Motivated by the important role of attention in human perception, we tackle this limitation by introducing unsupervised attention mechanisms that are jointly adversarialy trained with the generators and discriminators. We demonstrate qualitatively and quantitatively that our approach is able to attend to relevant regions in the image without requiring supervision, and that by doing so it achieves more realistic mappings compared to recent approaches.

Connected through hyperedge HE-1 (induced by source paper "unpaired image to image translation using adversarial consistency loss"):
  with members:
    - "breaking the cycle colleagues are all you need"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "combogan unrestrained scalability for image domain translation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-2 (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"
    - "towards instance level image to image translation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "joint wasserstein distribution matching"):
  with members:
    - "unsupervised video to video translation"
    - "dual reconstruction nets for image super resolution with gradient sensitive loss"
    - "improving shape deformation in unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "deep view morphing"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-4 (induced by source paper "generating object stamps"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "adaptive composition gan towards realistic image synthesis"
    - "semantic image synthesis with spatially adaptive normalization"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "multimodal unsupervised image to image translation"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-5 (induced by source paper "dual attention gan for large pose face frontalization"):
  with members:
    - "joint super resolution and alignment of tiny faces"
    - "cr gan learning complete representations for multi view generation"
    - "learning a high fidelity pose invariant model for high resolution face frontalization"
    - "cbam convolutional block attention module"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "attention is all you need"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"

Connected through hyperedge HE-6 (induced by source paper "cycle in cycle generative adversarial networks for keypoint guided image generation"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "gazecorrection self guided eye manipulation in the wild using self supervised generative adversarial networks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cascade attention guided residue learning gan for cross modal translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "video to video synthesis"
    - "cross view image synthesis using conditional gans"

Connected through hyperedge HE-7 (induced by source paper "local class specific and global image level generative adversarial networks for semantic guided scene generation"):
  with members:
    - "seeing what a gan cannot generate"
    - "progressive pose attention transfer for person image generation"
    - "a layer based sequential framework for scene generation with gans"
    - "bridging the domain gap for ground to aerial image matching"
    - "singan learning a generative model from a single natural image"
    - "autogan neural architecture search for generative adversarial networks"
    - "coco gan generation by parts via conditional coordinating"
    - "few shot unsupervised image to image translation"

Connected through hyperedge HE-8 (induced by source paper "hologan unsupervised learning of 3d representations from natural images"):
  with members:
    - "representation learning by rotating your faces"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "spectral normalization for generative adversarial networks"
    - "towards open set identity preserving face synthesis"

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)