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: "mid fusion octree based object level multi instance dynamic slam"
  Abstract: We propose a new multi-instance dynamic RGB-D SLAM system using an object-level octree-based volumetric representation. It can provide robust camera tracking in dynamic environments and at the same time, continuously estimate geometric, semantic, and motion properties for arbitrary objects in the scene. For each incoming frame, we perform instance segmentation to detect objects and refine mask boundaries using geometric and motion information. Meanwhile, we estimate the pose of each existing moving object using an object-oriented tracking method and robustly track the camera pose against the static scene. Based on the estimated camera pose and object poses, we associate segmented masks with existing models and incrementally fuse corresponding colour, depth, semantic, and foreground object probabilities into each object model. In contrast to existing approaches, our system is the first system to generate an object-level dynamic volumetric map from a single RGB-D camera, which can be used directly for robotic tasks. Our method can run at 2-3 Hz on a CPU, excluding the instance segmentation part. We demonstrate its effectiveness by quantitatively and qualitatively testing it on both synthetic and real-world sequences.

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"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "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 "learning densities in feature space for reliable segmentation of indoor scenes"):
  with members:
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "fusion volumetric object level slam"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"

Connected through hyperedge HE-5 (induced by source paper "panopticfusion online volumetric semantic mapping at the level of stuff and things"):
  with members:
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "fusion volumetric object level slam"
    - "3d sis 3d semantic instance segmentation of rgb d scans"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "tangent convolutions for dense prediction in 3d"

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"
    - "joint 3d proposal generation and object detection from view aggregation"

Connected through hyperedge HE-7 (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"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "em fusion dynamic object level slam with probabilistic data association"):
  with members:
    - "fusion volumetric object level slam"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "mask r cnn"
    - "probabilistic articulated real time tracking for robot manipulation"
    - "reconstructing articulated rigged models from rgb d videos"

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.RO(Robotics)