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: "multi view deep learning for consistent semantic mapping with rgb d cameras"
  Abstract: Visual scene understanding is an important capability that enables robots to purposefully act in their environment. In this paper, we propose a novel approach to object-class segmentation from multiple RGB-D views using deep learning. We train a deep neural network to predict object-class semantics that is consistent from several view points in a semi-supervised way. At test time, the semantics predictions of our network can be fused more consistently in semantic keyframe maps than predictions of a network trained on individual views. We base our network architecture on a recent single-view deep learning approach to RGB and depth fusion for semantic object-class segmentation and enhance it with multi-scale loss minimization. We obtain the camera trajectory using RGB-D SLAM and warp the predictions of RGB-D images into ground-truth annotated frames in order to enforce multi-view consistency during training. At test time, predictions from multiple views are fused into keyframes. We propose and analyze several methods for enforcing multi-view consistency during training and testing. We evaluate the benefit of multi-view consistency training and demonstrate that pooling of deep features and fusion over multiple views outperforms single-view baselines on the NYUDv2 benchmark for semantic segmentation. Our end-to-end trained network achieves state-of-the-art performance on the NYUDv2 dataset in single-view segmentation as well as multi-view semantic fusion.

Connected through hyperedge HE-1 (induced by source paper "fusion aware point convolution for online semantic 3d scene segmentation"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "active scene understanding via online semantic reconstruction"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "real time progressive 3d semantic segmentation for indoor scene"

Connected through hyperedge HE-2 (induced by source paper "comparing view based and map based semantic labelling in real time slam"):
  with members:
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "orthographic feature transform for monocular 3d object detection"
    - "octnetfusion learning depth fusion from data"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

Connected through hyperedge HE-3 (induced by source paper "a review on deep learning techniques for 3d sensed data classification"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "context prediction for unsupervised deep learning on point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-4 (induced by source paper "what synthesis is missing depth adaptation integrated with weak supervision for indoor scene parsing"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "weakly and semi supervised panoptic segmentation"
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"):
  with members:
    - "multi stream cnn based video semantic segmentation for automated driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"
    - "digrad multi task reinforcement learning with shared actions"
    - "universal representations the missing link between faces text planktons and cat breeds"

Connected through hyperedge HE-6 (induced by source paper "casting geometric constraints in semantic segmentation as semi supervised learning"):
  with members:
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "open3d a modern library for 3d data processing"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "digging into self supervised monocular depth estimation"

Connected through hyperedge HE-7 (induced by source paper "rgb d odometry and slam"):
  with members:
    - "cam convs camera aware multi scale convolutions for single view depth"
    - "direct sparse mapping"
    - "deeptam deep tracking and mapping"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "planematch patch coplanarity prediction for robust rgb d reconstruction"
    - "rgbid slam for accurate real time localisation and 3d mapping"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"

Connected through hyperedge HE-8 (induced by source paper "semi supervised semantic mapping through label propagation with semantic texture meshes"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "splatnet sparse lattice networks for point cloud processing"
    - "tangent convolutions for dense prediction in 3d"
    - "semantic visual localization"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "suggestive annotation a deep active learning framework for biomedical image 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)