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: "genegan learning object transfiguration and attribute subspace from unpaired data"
  Abstract: Object Transfiguration replaces an object in an image with another object from a second image. For example it can perform tasks like "putting exactly those eyeglasses from image A on the nose of the person in image B". Usage of exemplar images allows more precise specification of desired modifications and improves the diversity of conditional image generation. However, previous methods that rely on feature space operations, require paired data and/or appearance models for training or disentangling objects from background. In this work, we propose a model that can learn object transfiguration from two unpaired sets of images: one set containing images that "have" that kind of object, and the other set being the opposite, with the mild constraint that the objects be located approximately at the same place. For example, the training data can be one set of reference face images that have eyeglasses, and another set of images that have not, both of which spatially aligned by face landmarks. Despite the weak 0/1 labels, our model can learn an "eyeglasses" subspace that contain multiple representatives of different types of glasses. Consequently, we can perform fine-grained control of generated images, like swapping the glasses in two images by swapping the projected components in the "eyeglasses" subspace, to create novel images of people wearing eyeglasses. #R##N#Overall, our deterministic generative model learns disentangled attribute subspaces from weakly labeled data by adversarial training. Experiments on CelebA and Multi-PIE datasets validate the effectiveness of the proposed model on real world data, in generating images with specified eyeglasses, smiling, hair styles, and lighting conditions etc. The code is available online.

Connected through hyperedge HE-1 (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-2 (induced by source paper "semantic adversarial attacks parametric transformations that fool deep classifiers"):
  with members:
    - "spatially transformed adversarial examples"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "robustness via curvature regularization and vice versa"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "fast geometrically perturbed adversarial faces"
    - "adversarial vulnerability for any classifier"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "targeted nonlinear adversarial perturbations in images and videos"

Connected through hyperedge HE-3 (induced by source paper "clsgan selective attribute editing based on classification adversarial network"):
  with members:
    - "attribute guided unpaired image to image translation with semi supervised learning"
    - "fully featured attribute transfer"
    - "image to image translation with multi path consistency regularization"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "ugan untraceable gan for multi domain face translation"
    - "relgan multi domain image to image translation via relative attributes"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "mulgan facial attribute editing by exemplar"):
  with members:
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "dna gan learning disentangled representations from multi attribute images"
    - "attgan facial attribute editing by only changing what you want"
    - "progressive growing of gans for improved quality stability and variation"
    - "unsupervised image to image translation networks"
    - "improved training of wasserstein gans"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-5 (induced by source paper "covering up bias in celeba like datasets with markov blankets a post hoc cure for attribute prior avoidance"):
  with members:
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "generative adversarial interpolative autoencoding adversarial training on latent space interpolations encourages convex latent distributions"
    - "dna gan learning disentangled representations from multi attribute images"
    - "progressive growing of gans for improved quality stability and variation"
    - "fairness in machine learning lessons from political philosophy"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "a deeper look at dataset bias"
    - "nice non linear independent components estimation"

Connected through hyperedge HE-6 (induced by source paper "style separation and synthesis via generative adversarial networks"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "pose guided person image generation"
    - "unsupervised generative adversarial cross modal hashing"
    - "one shot fine grained instance retrieval"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"

Connected through hyperedge HE-7 (induced by source paper "elegant exchanging latent encodings with gan for transferring multiple face attributes"):
  with members:
    - "modular generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "dna gan learning disentangled representations from multi attribute images"
    - "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"
    - "toward multimodal image to image translation"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-8 (induced by source paper "generative adversarial networks for image to image translation on multi contrast mr images a comparison of cyclegan and unit"):
  with members:
    - "few shot unsupervised image to image translation"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "dualgan unsupervised dual learning for image to image translation"
    - "image to image translation with conditional adversarial networks"

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)