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: "codeslam learning a compact optimisable representation for dense visual slam"
  Abstract: The representation of geometry in real-time 3D perception systems continues to be a critical research issue. Dense maps capture complete surface shape and can be augmented with semantic labels, but their high dimensionality makes them computationally costly to store and process, and unsuitable for rigorous probabilistic inference. Sparse feature-based representations avoid these problems, but capture only partial scene information and are mainly useful for localisation only. #R##N#We present a new compact but dense representation of scene geometry which is conditioned on the intensity data from a single image and generated from a code consisting of a small number of parameters. We are inspired by work both on learned depth from images, and auto-encoders. Our approach is suitable for use in a keyframe-based monocular dense SLAM system: While each keyframe with a code can produce a depth map, the code can be optimised efficiently jointly with pose variables and together with the codes of overlapping keyframes to attain global consistency. Conditioning the depth map on the image allows the code to only represent aspects of the local geometry which cannot directly be predicted from the image. We explain how to learn our code representation, and demonstrate its advantageous properties in monocular SLAM.

Connected through hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "self driving cars a survey"
    - "governing autonomous vehicles emerging responses for safety liability privacy cybersecurity and industry risks"
    - "pixor real time 3d object detection from point clouds"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "3d point cloud denoising via deep neural network based local surface estimation"
    - "fvnet 3d front view proposal generation for real time object detection from point clouds"

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 "meshlet priors for 3d mesh reconstruction"):
  with members:
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "learning category specific mesh reconstruction from image collections"
    - "deepmvs learning multi view stereopsis"
    - "learning 3d shape completion under weak supervision"

Connected through hyperedge HE-4 (induced by source paper "routedfusion learning real time depth map fusion"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "surfelmeshing online surfel based mesh reconstruction"
    - "mesh r cnn"
    - "deeptam deep tracking and mapping"
    - "learning implicit fields for generative shape modeling"

Connected through hyperedge HE-5 (induced by source paper "scalable uncertainty for computer vision with functional variational inference"):
  with members:
    - "deepfactors real time probabilistic dense monocular slam"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "functional variational bayesian neural networks"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "efficient uncertainty estimation for semantic segmentation in videos"

Connected through hyperedge HE-6 (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"
    - "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"
    - "cnn slam real time dense monocular slam with learned depth prediction"

Connected through hyperedge HE-7 (induced by source paper "bundle adjustment on a graph processor"):
  with members:
    - "futuremapping 2 gaussian belief propagation for spatial ai"
    - "dissecting the graphcore ipu architecture via microbenchmarking"
    - "navigating the landscape for real time localization and mapping for robotics and virtual and augmented reality"
    - "futuremapping the computational structure of spatial ai systems"
    - "direct sparse odometry"
    - "introducing slambench a performance and accuracy benchmarking methodology for slam"
    - "orb slam a versatile and accurate monocular slam system"
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-8 (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"
    - "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"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

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)