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: "scenecut joint geometric and object segmentation for indoor scenes"
  Abstract: This paper presents SceneCut, a novel approach to jointly discover previously unseen objects and non-object surfaces using a single RGB-D image. SceneCut's joint reasoning over scene semantics and geometry allows a robot to detect and segment object instances in complex scenes where modern deep learning-based methods either fail to separate object instances, or fail to detect objects that were not seen during training. SceneCut automatically decomposes a scene into meaningful regions which either represent objects or scene surfaces. The decomposition is qualified by an unified energy function over objectness and geometric fitting. We show how this energy function can be optimized efficiently by utilizing hierarchical segmentation trees. Moreover, we leverage a pre-trained convolutional oriented boundary network to predict accurate boundaries from images, which are used to construct high-quality region hierarchies. We evaluate SceneCut on several different indoor environments, and the results show that SceneCut significantly outperforms all the existing 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"
    - "w net a deep model for fully unsupervised image segmentation"
    - "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 "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-3 (induced by source paper "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "easylabel a semi automatic pixel wise object annotation tool for creating robotic rgb d datasets"
    - "towards segmenting everything that moves"
    - "large scale interactive object segmentation with human annotators"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "shapemask learning to segment novel objects by refining shape priors"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "group normalization"

Connected through hyperedge HE-4 (induced by source paper "volumetric instance aware semantic mapping and 3d object discovery"):
  with members:
    - "fast and accurate semantic mapping through geometric based incremental segmentation"
    - "fusion volumetric object level slam"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "real time progressive 3d semantic segmentation for indoor scene"
    - "incremental object database building 3d models from multiple partial observations"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "learning to segment every thing"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

Connected through hyperedge HE-5 (induced by source paper "inferring occluded geometry improves performance when retrieving an object from dense clutter"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "semantic segmentation from limited training data"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "mask r cnn"
    - "semantic scene completion from a single depth image"
    - "a point set generation network for 3d object reconstruction from a single image"

Connected through hyperedge HE-6 (induced by source paper "easylabel a semi automatic pixel wise object annotation tool for creating robotic rgb d datasets"):
  with members:
    - "recognizing objects in the wild where do we stand"
    - "labelfusion a pipeline for generating ground truth labels for real rgbd data of cluttered scenes"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "rgbd datasets past present and future"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "semanticpaint interactive 3d labeling and learning at your fingertips"

Connected through hyperedge HE-7 (induced by source paper "pointsift a sift like network module for 3d point cloud semantic segmentation"):
  with members:
    - "dynamic graph cnn for learning on point clouds"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "octnet learning deep 3d representations at high resolutions"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "fpnn field probing neural networks for 3d data"

Connected through hyperedge HE-8 (induced by source paper "bayesian semantic instance segmentation in open set world"):
  with members:
    - "semantic segmentation from limited training data"
    - "semantic instance segmentation via deep metric learning"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "yolo9000 better faster stronger"
    - "deep watershed transform for instance segmentation"
    - "end to end instance segmentation with recurrent attention"
    - "fully convolutional instance aware 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)