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: "semi dense 3d semantic mapping from monocular slam"
  Abstract: The bundle of geometry and appearance in computer vision has proven to be a promising solution for robots across a wide variety of applications. Stereo cameras and RGB-D sensors are widely used to realise fast 3D reconstruction and trajectory tracking in a dense way. However, they lack flexibility of seamless switch between different scaled environments, i.e., indoor and outdoor scenes. In addition, semantic information are still hard to acquire in a 3D mapping. We address this challenge by combining the state-of-art deep learning method and semi-dense Simultaneous Localisation and Mapping (SLAM) based on video stream from a monocular camera. In our approach, 2D semantic information are transferred to 3D mapping via correspondence between connective Keyframes with spatial consistency. There is no need to obtain a semantic segmentation for each frame in a sequence, so that it could achieve a reasonable computation time. We evaluate our method on indoor/outdoor datasets and lead to an improvement in the 2D semantic labelling over baseline single frame predictions.

Connected through hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "learning monocular depth estimation infusing traditional stereo knowledge"
    - "deep direct visual odometry"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "multi frame gan image enhancement for stereo visual odometry in low light"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "sganvo unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "extending maps with semantic and contextual object information for robot navigation a learning based framework using visual and depth cues"):
  with members:
    - "kaolin a pytorch library for accelerating 3d deep learning research"
    - "yolact real time instance segmentation"
    - "mapping road lanes using laser remission and deep neural networks"
    - "end to end recovery of human shape and pose"
    - "mix and match tuning for self supervised semantic segmentation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "pedestrian prediction by planning using deep neural networks"
    - "mask r cnn"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "network uncertainty informed semantic feature selection for visual slam"):
  with members:
    - "utilizing semantic visual landmarks for precise vehicle navigation"
    - "long term visual localization using semantically segmented images"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "slam with objects using a nonparametric pose graph"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"

Connected through hyperedge HE-5 (induced by source paper "fast and accurate semantic mapping through geometric based incremental segmentation"):
  with members:
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "rethinking atrous convolution for semantic image segmentation"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "real time monocular object slam"

Connected through hyperedge HE-6 (induced by source paper "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "probabilistic global scale estimation for monoslam based on generic object detection"
    - "3d bounding box estimation using deep learning and geometry"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "visual inertial scene representations"
    - "you only look once unified real time object detection"
    - "monocular slam supported object recognition"

Connected through hyperedge HE-7 (induced by source paper "multimodal sensor based semantic 3d mapping for a large scale environment"):
  with members:
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "meaningful maps with object oriented semantic mapping"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "fully convolutional networks for semantic segmentation"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-8 (induced by source paper "vision based semantic mapping and localization for autonomous indoor parking"):
  with members:
    - "direct sparse odometry"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "pvanet lightweight deep neural networks for real time object detection"
    - "orb slam a versatile and accurate monocular slam system"

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)