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: "w net a deep model for fully unsupervised image segmentation"
  Abstract: While significant attention has been recently focused on designing supervised deep semantic segmentation algorithms for vision tasks, there are many domains in which sufficient supervised pixel-level labels are difficult to obtain. In this paper, we revisit the problem of purely unsupervised image segmentation and propose a novel deep architecture for this problem. We borrow recent ideas from supervised semantic segmentation methods, in particular by concatenating two fully convolutional networks together into an autoencoder--one for encoding and one for decoding. The encoding layer produces a k-way pixelwise prediction, and both the reconstruction error of the autoencoder as well as the normalized cut produced by the encoder are jointly minimized during training. When combined with suitable postprocessing involving conditional random field smoothing and hierarchical segmentation, our resulting algorithm achieves impressive results on the benchmark Berkeley Segmentation Data Set, outperforming a number of competing methods.

Connected through hyperedge HE-1 (induced by source paper "mumford shah loss functional for image segmentation with deep learning"):
  with members:
    - "unsupervised segmentation of 3d medical images based on clustering and deep representation learning"
    - "adversarial learning for semi supervised semantic segmentation"
    - "scenecut joint geometric and object segmentation for indoor scenes"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"
    - "a fully convolutional neural network for cardiac segmentation in short axis mri"
    - "u net convolutional networks for biomedical image segmentation"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "bae net branched autoencoder for shape co segmentation"):
  with members:
    - "cosegnet deep co segmentation of 3d shapes with group consistency loss"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deep part induction from articulated object pairs"
    - "3d point capsule networks"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "adversarial complementary learning for weakly supervised object localization"

Connected through hyperedge HE-3 (induced by source paper "unsupervised object segmentation by redrawing"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "high fidelity image generation with fewer labels"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "weakly supervised instance segmentation using class peak response"
    - "vector image generation by learning parametric layer decomposition"
    - "learning to segment via cut and paste"
    - "seigan towards compositional image generation by simultaneously learning to segment enhance and inpaint"

Connected through hyperedge HE-4 (induced by source paper "emergence of object segmentation in perturbed generative models"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "alphagan generative adversarial networks for natural image matting"
    - "learning to segment via cut and paste"
    - "seigan towards compositional image generation by simultaneously learning to segment enhance and inpaint"
    - "a style based generator architecture for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep image matting"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "metric based regularization and temporal ensemble for multi task learning using heterogeneous unsupervised tasks"):
  with members:
    - "transfer adaptation learning a decade survey"
    - "boosting self supervised learning via knowledge transfer"
    - "improvements to context based self supervised learning"
    - "multi task self supervised visual learning"
    - "a survey on deep learning in medical image analysis"
    - "a survey on multi task learning"
    - "deep retinal image understanding"
    - "generic 3d representation via pose estimation and matching"

Connected through hyperedge HE-7 (induced by source paper "incremental class discovery for semantic segmentation with rgbd sensing"):
  with members:
    - "fast and accurate semantic mapping through geometric based incremental segmentation"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "segcloud semantic segmentation of 3d point clouds"
    - "pointwise convolutional neural networks"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-8 (induced by source paper "hr sar net a deep neural network for urban scene segmentation from high resolution sar data"):
  with members:
    - "the sen1 2 dataset for deep learning in sar optical data fusion"
    - "road segmentation in sar satellite images with deep fully convolutional neural networks"
    - "deep structured features for semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"

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)