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: "cnn slam real time dense monocular slam with learned depth prediction"
  Abstract: Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted depth maps from a deep neural network can be deployed for accurate and dense monocular reconstruction. We propose a method where CNN-predicted dense depth maps are naturally fused together with depth measurements obtained from direct monocular SLAM. Our fusion scheme privileges depth prediction in image locations where monocular SLAM approaches tend to fail, e.g. along low-textured regions, and vice-versa. We demonstrate the use of depth prediction for estimating the absolute scale of the reconstruction, hence overcoming one of the major limitations of monocular SLAM. Finally, we propose a framework to efficiently fuse semantic labels, obtained from a single frame, with dense SLAM, yielding semantically coherent scene reconstruction from a single view. Evaluation results on two benchmark datasets show the robustness and accuracy of our approach.

Connected through hyperedge HE-1 (induced by source paper "image matching across wide baselines from paper to practice"):
  with members:
    - "self supervised learning of depth and ego motion with differentiable bundle adjustment"
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "explicit spatial encoding for deep local descriptors"
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "neural guided ransac learning where to sample model hypotheses"
    - "nm net mining reliable neighbors for robust feature correspondences"
    - "understanding the limitations of cnn based absolute camera pose regression"

Connected through hyperedge HE-2 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry"):
  with members:
    - "visual inertial mapping with non linear factor recovery"
    - "panoptic segmentation"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "multi frame gan image enhancement for stereo visual odometry in low light"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "visual odometry revisited what should be learnt"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning of depth optical flow and pose with occlusion from 3d geometry"):
  with members:
    - "beyond photometric loss for self supervised ego motion estimation"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks"
    - "lego learning edge with geometry all at once by watching videos"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"

Connected through hyperedge HE-4 (induced by source paper "deepfactors real time probabilistic dense monocular slam"):
  with members:
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "deeptam deep tracking and mapping"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "ls net learning to solve nonlinear least squares for monocular stereo"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "unsupervised learning of depth and ego motion from video"
    - "demon depth and motion network for learning monocular stereo"

Connected through hyperedge HE-5 (induced by source paper "autonomous planning based on spatial concepts to tidy up home environments with service robots"):
  with members:
    - "yolov3 an incremental improvement"
    - "multimodal sensor based semantic 3d mapping for a large scale environment"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "spatial concept acquisition for a mobile robot that integrates self localization and unsupervised word discovery from spoken sentences"
    - "ssd single shot multibox detector"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "the apolloscape open dataset for autonomous driving and its application"):
  with members:
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deepmvs learning multi view stereopsis"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-7 (induced by source paper "cam convs camera aware multi scale convolutions for single view depth"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deeptam deep tracking and mapping"
    - "deep attention based classification network for robust depth prediction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-8 (induced by source paper "3d instance segmentation via multi task metric learning"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "pixor real time 3d object detection from point clouds"
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "3d sis 3d semantic instance segmentation of rgb d scans"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"

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)