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 object segmentation by redrawing"
  Abstract: Object segmentation is a crucial problem that is usually solved by using supervised learning approaches over very large datasets composed of both images and corresponding object masks. Since the masks have to be provided at pixel level, building such a dataset for any new domain can be very time-consuming. We present ReDO, a new model able to extract objects from images without any annotation in an unsupervised way. It relies on the idea that it should be possible to change the textures or colors of the objects without changing the overall distribution of the dataset. Following this assumption, our approach is based on an adversarial architecture where the generator is guided by an input sample: given an image, it extracts the object mask, then redraws a new object at the same location. The generator is controlled by a discriminator that ensures that the distribution of generated images is aligned to the original one. We experiment with this method on different datasets and demonstrate the good quality of extracted masks.

Connected through hyperedge HE-1 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "a layer based sequential framework for scene generation with gans"
    - "transformable bottleneck networks"
    - "generating multiple objects at spatially distinct locations"
    - "neural voxel renderer learning an accurate and controllable rendering tool"
    - "emergence of object segmentation in perturbed generative models"
    - "escaping plato s cave 3d shape from adversarial rendering"

Connected through hyperedge HE-2 (induced by source paper "instance separation emerges from inpainting"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "learning correspondence from the cycle consistency of time"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "selfie self supervised pretraining for image embedding"
    - "structureflow image inpainting via structure aware appearance flow"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "noise2void learning denoising from single noisy images"
    - "seigan towards compositional image generation by simultaneously learning to segment enhance and inpaint"

Connected through hyperedge HE-3 (induced by source paper "genesis generative scene inference and sampling with object centric latent representations"):
  with members:
    - "emergence of object segmentation in perturbed generative models"
    - "monet unsupervised scene decomposition and representation"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "unsupervised discovery of parts structure and dynamics"
    - "multi object representation learning with iterative variational inference"
    - "shaping belief states with generative environment models for rl"

Connected through hyperedge HE-4 (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"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "unsupervised object segmentation with explicit localization module"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "semi convolutional operators for instance segmentation"
    - "neural expectation maximization"
    - "learning features by watching objects move"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "investigating object compositionality in generative adversarial networks"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "emergence of object segmentation in perturbed generative models"
    - "multi object representation learning with iterative variational inference"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "object discovery with a copy pasting gan"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the gan landscape losses architectures regularization and normalization"
    - "relational deep reinforcement learning"

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)