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: "improving shape deformation in unsupervised image to image translation"
  Abstract: Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically unsuccessful when the domains require larger shape change. Inspired by semantic segmentation, we introduce a discriminator with dilated convolutions that is able to use information from across the entire image to train a more context-aware generator. This is coupled with a multi-scale perceptual loss that is better able to represent error in the underlying shape of objects. We demonstrate that this design is more capable of representing shape deformation in a challenging toy dataset, plus in complex mappings with significant dataset variation between humans, dolls, and anime faces, and between cats and dogs.

Connected through hyperedge HE-1 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "transgaga geometry aware unsupervised image to image translation"
    - "emergence of object segmentation in perturbed generative models"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "learning conditional deformable templates with convolutional networks"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "voxelmorph a learning framework for deformable medical image registration"

Connected through hyperedge HE-2 (induced by source paper "unity style transfer for person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "multi level factorisation net for person re identification"
    - "spatial temporal person re identification"
    - "harmonious attention network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"

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 gans using optimal transport"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "unsupervised attention guided image to image translation"
    - "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 "ganhopper multi hop gan for unsupervised image to image translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "carigans unpaired photo to caricature translation"
    - "logan unpaired shape transform in latent overcomplete space"
    - "multimodal unsupervised image to image translation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "attentiongan unpaired image to image translation using attention guided generative adversarial networks"):
  with members:
    - "show attend and translate unsupervised image translation with self regularization and attention"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-6 (induced by source paper "transgaga geometry aware unsupervised image to image translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "synthesizing images of humans in unseen poses"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "every smile is unique landmark guided diverse smile generation"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "unsupervised discovery of object landmarks as structural representations"

Connected through hyperedge HE-7 (induced by source paper "attribute driven spontaneous motion in unpaired image translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "soft gated warping gan for pose guided person image synthesis"
    - "multimodal unsupervised image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "instance level facial attributes transfer with geometry aware flow"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "semi parametric image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "expression conditional gan for facial expression to expression translation"):
  with members:
    - "attribute guided sketch generation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "recycle gan unsupervised video retargeting"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "large scale gan training for high fidelity natural image 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)