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: "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
  Abstract: We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories. To disentangle the factors without supervision, our key idea is to use information theory to associate each factor to a latent code, and to condition the relationships between the codes in a specific way to induce the desired hierarchy. Through extensive experiments, we show that FineGAN achieves the desired disentanglement to generate realistic and diverse images belonging to fine-grained classes of birds, dogs, and cars. Using FineGAN's automatically learned features, we also cluster real images as a first attempt at solving the novel problem of unsupervised fine-grained object category discovery. Our code/models/demo can be found at this https URL

Connected through hyperedge HE-1 (induced by source paper "object centric image generation from layouts"):
  with members:
    - "interactive image generation using scene graphs"
    - "scene graph generation with external knowledge and image reconstruction"
    - "object driven text to image synthesis via adversarial training"
    - "image synthesis from reconfigurable layout and style"
    - "pastegan a semi parametric method to generate image from scene graph"
    - "locality and compositionality in zero shot learning"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"

Connected through hyperedge HE-2 (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"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "controlling style and semantics in weakly supervised image generation"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "chatpainter improving text to image generation using dialogue"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "tell draw and repeat generating and modifying images based on continual linguistic instruction"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "mixnmatch multifactor disentanglement and encoding for conditional image generation"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "synthesizing images of humans in unseen poses"
    - "deformable generator network unsupervised disentanglement of appearance and geometry"
    - "multimodal unsupervised image to image translation"
    - "a variational u net for conditional appearance and shape generation"

Connected through hyperedge HE-6 (induced by source paper "g 3 an this video does not exist disentangling motion and appearance for video generation"):
  with members:
    - "video generation from single semantic label map"
    - "guided image to image translation with bi directional feature transformation"
    - "pose guided human video generation"
    - "learning to forecast and refine residual motion for image to video generation"
    - "smit stochastic multi label image to image translation"
    - "video prediction with appearance and motion conditions"
    - "flow grounded spatial temporal video prediction from still images"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "hi cmd hierarchical cross modality disentanglement for visible infrared person re identification"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "unsupervised person image generation with semantic parsing transformation"
    - "illumination adaptive person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "gait recognition via disentangled representation learning"
    - "decorrelated adversarial learning for age invariant face recognition"
    - "re identification with consistent attentive siamese networks"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-8 (induced by source paper "onegan simultaneous unsupervised learning of conditional image generation foreground segmentation and fine grained clustering"):
  with members:
    - "unsupervised learning of foreground object segmentation"
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "deep high resolution representation learning for visual recognition"
    - "unsupervised object segmentation by redrawing"
    - "unsupervised video object segmentation with distractor aware online adaptation"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "mixup beyond empirical risk minimization"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"

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)