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: "co fusion real time segmentation tracking and fusion of multiple objects"
  Abstract: In this paper we introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-D images as input and segments the scene into different objects (using either motion or semantic cues) while simultaneously tracking and reconstructing their 3D shape in real time. We use a multiple model fitting approach where each object can move independently from the background and still be effectively tracked and its shape fused over time using only the information from pixels associated with that object label. Previous attempts to deal with dynamic scenes have typically considered moving regions as outliers, and consequently do not model their shape or track their motion over time. In contrast, we enable the robot to maintain 3D models for each of the segmented objects and to improve them over time through fusion. As a result, our system can enable a robot to maintain a scene description at the object level which has the potential to allow interactions with its working environment; even in the case of dynamic scenes.

Connected through hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans"):
  with members:
    - "teaser fast and certifiable point cloud registration"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "panoptic segmentation"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "semi supervised semantic mapping through label propagation with semantic texture meshes"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "value iteration networks on multiple levels of abstraction"

Connected through hyperedge HE-2 (induced by source paper "dymslam 4d dynamic scene reconstruction based on geometrical motion segmentation"):
  with members:
    - "track to reconstruct and reconstruct to track"
    - "stereo based multi motion visual odometry for mobile robots"
    - "unsupervised monocular depth and ego motion learning with structure and semantics"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "ds slam a semantic visual slam towards dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "multimotion visual odometry mvo simultaneous estimation of camera and third party motions"

Connected through hyperedge HE-3 (induced by source paper "flowfusion dynamic dense rgb d slam based on optical flow"):
  with members:
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "ds slam a semantic visual slam towards dynamic environments"
    - "mid fusion octree based object level multi instance dynamic slam"
    - "optical flow in mostly rigid scenes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "learning to refine object segments"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-4 (induced by source paper "kimera an open source library for real time metric semantic localization and mapping"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "dual graph convolutional network for semantic segmentation"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "incremental visual inertial 3d mesh generation with structural regularities"
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "active scene understanding via online semantic reconstruction"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"

Connected through hyperedge HE-5 (induced by source paper "resolving 3d human pose ambiguities with 3d scene constraints"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "estimating 3d motion and forces of person object interactions from monocular video"
    - "learning joint reconstruction of hands and manipulated objects"
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "expressive body capture 3d hands face and body from a single image"
    - "imapper interaction guided scene mapping from monocular videos"
    - "open3d a modern library for 3d data processing"
    - "learning to estimate 3d human pose and shape from a single color image"

Connected through hyperedge HE-6 (induced by source paper "live reconstruction of large scale dynamic outdoor worlds"):
  with members:
    - "pointflownet learning representations for rigid motion estimation from point clouds"
    - "fusion volumetric object level slam"
    - "open3d a modern library for 3d data processing"
    - "yolov3 an incremental improvement"
    - "robust dense mapping for large scale dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "pyramid stereo matching network"
    - "mid fusion octree based object level multi instance dynamic slam"

Connected through hyperedge HE-7 (induced by source paper "dal a deep depth aware long term tracker"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "atom accurate tracking by overlap maximization"
    - "depth masked discriminative correlation filter"
    - "depth aware cnn for rgb d segmentation"
    - "distractor aware siamese networks for visual object tracking"
    - "object tracking by reconstruction with view specific discriminative correlation filters"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-8 (induced by source paper "detectfusion detecting and segmenting both known and unknown dynamic objects in real time slam"):
  with members:
    - "fusion volumetric object level slam"
    - "yolov3 an incremental improvement"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "mid fusion octree based object level multi instance dynamic slam"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mask r cnn"
    - "yolo9000 better faster stronger"
    - "learning to refine object segments"

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)