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: "cvae gan fine grained image generation through asymmetric training"
  Abstract: We present variational generative adversarial networks, a general learning framework that combines a variational auto-encoder with a generative adversarial network, for synthesizing images in fine-grained categories, such as faces of a specific person or objects in a category. Our approach models an image as a composition of label and latent attributes in a probabilistic model. By varying the fine-grained category label fed into the resulting generative model, we can generate images in a specific category with randomly drawn values on a latent attribute vector. Our approach has two novel aspects. First, we adopt a cross entropy loss for the discriminative and classifier network, but a mean discrepancy objective for the generative network. This kind of asymmetric loss function makes the GAN training more stable. Second, we adopt an encoder network to learn the relationship between the latent space and the real image space, and use pairwise feature matching to keep the structure of generated images. We experiment with natural images of faces, flowers, and birds, and demonstrate that the proposed models are capable of generating realistic and diverse samples with fine-grained category labels. We further show that our models can be applied to other tasks, such as image inpainting, super-resolution, and data augmentation for training better face recognition models.

Connected through hyperedge HE-1 (induced by source paper "algorithmic fairness"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "fairface face attribute dataset for balanced race gender and age"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "generative adversarial networks for mitigating biases in machine learning systems"
    - "improved adversarial learning for fair classification"
    - "compositional fairness constraints for graph embeddings"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"

Connected through hyperedge HE-2 (induced by source paper "medgan medical image translation using gans"):
  with members:
    - "generative adversarial network in medical imaging a review"
    - "towards a deeper understanding of adversarial losses"
    - "improving surgical training phantoms by hyperrealism deep unpaired image to image translation from real surgeries"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "adversarial sparse view cbct artifact reduction"
    - "voxelatlasgan 3d left ventricle segmentation on echocardiography with atlas guided generation and voxel to voxel discrimination"
    - "cross modality synthesis from ct to pet using fcn and gan networks for improved automated lesion detection"
    - "pros and cons of gan evaluation measures"

Connected through hyperedge HE-3 (induced by source paper "emerging disentanglement in auto encoder based unsupervised image content transfer"):
  with members:
    - "a two step disentanglement method"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "dida disentangled synthesis for domain adaptation"
    - "image to image translation for cross domain disentanglement"
    - "drit diverse image to image translation via disentangled representations"
    - "infovae information maximizing variational autoencoders"
    - "emergence of invariance and disentanglement in deep representations"

Connected through hyperedge HE-4 (induced by source paper "piigan generative adversarial networks for pluralistic image inpainting"):
  with members:
    - "learning pyramid context encoder network for high quality image inpainting"
    - "coherent semantic attention for image inpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "shift net image inpainting via deep feature rearrangement"
    - "image inpainting via generative multi column convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "matchinggan matching based few shot image generation"):
  with members:
    - "figr few shot image generation with reptile"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "few shot unsupervised image to image translation"
    - "cgans with projection discriminator"
    - "on first order meta learning algorithms"
    - "spectral normalization for generative adversarial networks"
    - "meta transfer learning for few shot learning"
    - "an empirical study on evaluation metrics of generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "human to ai coach improving human inputs to ai systems"):
  with members:
    - "personalized explanation in machine learning"
    - "counterfactual visual explanations"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "generative image inpainting with contextual attention"
    - "image to image translation with conditional adversarial networks"
    - "generative visual manipulation on the natural image manifold"
    - "k sparse autoencoders"

Connected through hyperedge HE-7 (induced by source paper "fine grained synthesis of unrestricted adversarial examples"):
  with members:
    - "neural puppet generative layered cartoon characters"
    - "certified adversarial robustness via randomized smoothing"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "spatially transformed adversarial examples"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial patch"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-8 (induced by source paper "learning to dress 3d people in generative clothing"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "facsimile fast and accurate scans from an image in less than a second"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "deephuman 3d human reconstruction from a single image"
    - "multi garment net learning to dress 3d people from images"
    - "moulding humans non parametric 3d human shape estimation from single images"
    - "learning to train with synthetic humans"

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)