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: "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
  Abstract: Systems which incrementally create 3D semantic maps from image sequences must store and update representations of both geometry and semantic entities. However, while there has been much work on the correct formulation for geometrical estimation, state-of-the-art systems usually rely on simple semantic representations which store and update independent label estimates for each surface element (depth pixels, surfels, or voxels). Spatial correlation is discarded, and fused label maps are incoherent and noisy. #R##N#We introduce a new compact and optimisable semantic representation by training a variational auto-encoder that is conditioned on a colour image. Using this learned latent space, we can tackle semantic label fusion by jointly optimising the low-dimenional codes associated with each of a set of overlapping images, producing consistent fused label maps which preserve spatial correlation. We also show how this approach can be used within a monocular keyframe based semantic mapping system where a similar code approach is used for geometry. The probabilistic formulation allows a flexible formulation where we can jointly estimate motion, geometry and semantics in a unified optimisation.

Connected through hyperedge HE-1 (induced by source paper "routedfusion learning real time depth map fusion"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "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"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "learning implicit fields for generative shape modeling"

Connected through hyperedge HE-2 (induced by source paper "comparing view based and map based semantic labelling in real time slam"):
  with members:
    - "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"
    - "da rnn semantic mapping with data associated recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "where are the keys learning object centric navigation policies on semantic maps with graph convolutional networks"):
  with members:
    - "graph policy gradients for large scale robot control"
    - "representation learning on graphs a reinforcement learning application"
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "deep slam object level rgbd slam based on class specific deep shape priors"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "fusion volumetric object level slam"

Connected through hyperedge HE-4 (induced by source paper "gradslam dense slam meets automatic differentiation"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "cubeslam monocular 3 d object slam"
    - "generalized inner loop meta learning"
    - "deeptam deep tracking and mapping"
    - "regnet learning the optimization of direct image to image pose registration"
    - "constructing category specific models for monocular object slam"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-5 (induced by source paper "scene aware audio rendering via deep acoustic analysis"):
  with members:
    - "data augmentation and deep convolutional neural networks for blind room acoustic parameter estimation"
    - "impulse response data augmentation and deep neural networks for blind room acoustic parameter estimation"
    - "regression and classification for direction of arrival estimation with convolutional recurrent neural networks"
    - "deeplight learning illumination for unconstrained mobile mixed reality"
    - "low frequency compensated synthetic impulse responses for improved far field speech recognition"
    - "improving reverberant speech training using diffuse acoustic simulation"
    - "scene aware audio for 360 videos"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-6 (induced by source paper "pan tilt zoom slam for sports videos"):
  with members:
    - "panoptic segmentation"
    - "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"
    - "sports camera calibration via synthetic data"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "a two point method for ptz camera calibration in sports"
    - "soccer on your tabletop"
    - "light cascaded convolutional neural networks for accurate player detection"
    - "backtracking regression forests for accurate camera relocalization"

Connected through hyperedge HE-7 (induced by source paper "futuremapping 2 gaussian belief propagation for spatial ai"):
  with members:
    - "visual inertial mapping with non linear factor recovery"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "futuremapping the computational structure of spatial ai systems"
    - "geometric deep learning going beyond euclidean data"
    - "distributed mapping with privacy and communication constraints lightweight algorithms and object based models"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"

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)