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: "da rnn semantic mapping with data associated recurrent neural networks"
  Abstract: 3D scene understanding is important for robots to interact with the 3D world in a meaningful way. Most previous works on 3D scene understanding focus on recognizing geometrical or semantic properties of the scene independently. In this work, we introduce Data Associated Recurrent Neural Networks (DA-RNNs), a novel framework for joint 3D scene mapping and semantic labeling. DA-RNNs use a new recurrent neural network architecture for semantic labeling on RGB-D videos. The output of the network is integrated with mapping techniques such as KinectFusion in order to inject semantic information into the reconstructed 3D scene. Experiments conducted on a real world dataset and a synthetic dataset with RGB-D videos demonstrate the ability of our method in semantic 3D scene mapping.

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 "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-3 (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-4 (induced by source paper "self supervised model adaptation for multimodal semantic segmentation"):
  with members:
    - "robot localization in floor plans using a room layout edge extraction network"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "in defense of classical image processing fast depth completion on the cpu"

Connected through hyperedge HE-5 (induced by source paper "simvodis simultaneous visual odometry object detection and instance segmentation"):
  with members:
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "fusion volumetric object level slam"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "ds slam a semantic visual slam towards dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "digging into self supervised monocular depth estimation"

Connected through hyperedge HE-6 (induced by source paper "panopticfusion online volumetric semantic mapping at the level of stuff and things"):
  with members:
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "fusion volumetric object level slam"
    - "3d sis 3d semantic instance segmentation of rgb d scans"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "tangent convolutions for dense prediction in 3d"

Connected through hyperedge HE-7 (induced by source paper "scenecode monocular dense semantic reconstruction using learned encoded scene representations"):
  with members:
    - "fusion volumetric object level slam"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "futuremapping the computational structure of spatial ai systems"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-8 (induced by source paper "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"):
  with members:
    - "yolov3 an incremental improvement"
    - "variational end to end navigation and localization"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "laser map aided visual inertial localization in changing environment"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "ground edge based lidar localization without a reflectivity calibration for autonomous driving"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "3d ssd learning hierarchical features from rgb d images for amodal 3d object detection"

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)