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: "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
  Abstract: Real-time, high-quality, 3D scanning of large-scale scenes is key to mixed reality and robotic applications. However, scalability brings challenges of drift in pose estimation, introducing significant errors in the accumulated model. Approaches often require hours of offline processing to globally correct model errors. Recent online methods demonstrate compelling results, but suffer from: (1) needing minutes to perform online correction preventing true real-time use; (2) brittle frame-to-frame (or frame-to-model) pose estimation resulting in many tracking failures; or (3) supporting only unstructured point-based representations, which limit scan quality and applicability. We systematically address these issues with a novel, real-time, end-to-end reconstruction framework. At its core is a robust pose estimation strategy, optimizing per frame for a global set of camera poses by considering the complete history of RGB-D input with an efficient hierarchical approach. We remove the heavy reliance on temporal tracking, and continually localize to the globally optimized frames instead. We contribute a parallelizable optimization framework, which employs correspondences based on sparse features and dense geometric and photometric matching. Our approach estimates globally optimized (i.e., bundle adjusted) poses in real-time, supports robust tracking with recovery from gross tracking failures (i.e., relocalization), and re-estimates the 3D model in real-time to ensure global consistency; all within a single framework. Our approach outperforms state-of-the-art online systems with quality on par to offline methods, but with unprecedented speed and scan completeness. Our framework leads to a comprehensive online scanning solution for large indoor environments, enabling ease of use and high-quality results.

Connected through hyperedge HE-1 (induced by source paper "occuseg occupancy aware 3d instance segmentation"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "associatively segmenting instances and semantics in point clouds"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "3d instance segmentation via multi task metric learning"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"

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 "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"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-4 (induced by source paper "adversarial texture optimization from rgb d scans"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "neural point based graphics"
    - "framenet learning local canonical frames of 3d surfaces from a single rgb image"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "robust watertight manifold surface generation method for shapenet models"
    - "perceptual losses for real time style transfer and super resolution"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "registration of multi view point sets under the perspective of expectation maximization"):
  with members:
    - "simultaneous merging multiple grid maps using the robust motion averaging"
    - "joint alignment of multiple point sets with batch and incremental expectation maximization"
    - "filterreg robust and efficient probabilistic point set registration using gaussian filter and twist parameterization"
    - "weighted motion averaging for the registration of multi view range scans"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "point set registration coherent point drift"
    - "on the closed form solution of the rotation matrix arising in computer vision problems"

Connected through hyperedge HE-6 (induced by source paper "stochastic triangular mesh mapping a terrain mapping technique for autonomous mobile robots"):
  with members:
    - "warped gaussian processes occupancy mapping with uncertain inputs"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-7 (induced by source paper "a review on deep learning techniques for 3d sensed data classification"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "context prediction for unsupervised deep learning on point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-8 (induced by source paper "3d scene reconstruction with multi layer depth and epipolar transformers"):
  with members:
    - "moulding humans non parametric 3d human shape estimation from single images"
    - "geometric pose affordance 3d human pose with scene constraints"
    - "peeking behind objects layered depth prediction from a single image"
    - "multiresolution tree networks for 3d point cloud processing"
    - "splatnet sparse lattice networks for point cloud processing"
    - "layer structured 3d scene inference via view synthesis"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"

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.GR(Graphics)