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: "visual inertial semantic scene representation for 3 d object detection"
  Abstract: We describe a system to detect objects in three-dimensional space using video and inertial sensors (accelerometer and gyrometer), ubiquitous in modern mobile platforms from phones to drones. Inertials afford the ability to impose class-specific scale priors for objects, and provide a global orientation reference. A minimal sufficient representation, the posterior of semantic (identity) and syntactic (pose) attributes of objects in space, can be decomposed into a geometric term, which can be maintained by a localization-and-mapping filter, and a likelihood function, which can be approximated by a discriminatively-trained convolutional neural network. The resulting system can process the video stream causally in real time, and provides a representation of objects in the scene that is persistent: Confidence in the presence of objects grows with evidence, and objects previously seen are kept in memory even when temporarily occluded, with their return into view automatically predicted to prime re-detection.

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 "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-3 (induced by source paper "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"):
  with members:
    - "visual inertial object detection and mapping"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "accurate single stage detector using recurrent rolling convolution"
    - "stacked hourglass networks for human pose estimation"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-4 (induced by source paper "cubeslam monocular 3d object detection and slam without prior models"):
  with members:
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "robust dense mapping for large scale dynamic environments"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "bayesian scale estimation for monocular slam based on generic object detection for correcting scale drift"
    - "direct monocular odometry using points and lines"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "pop up slam semantic monocular plane slam for low texture environments"

Connected through hyperedge HE-5 (induced by source paper "visual graphs from motion vgfm scene understanding with object geometry reasoning"):
  with members:
    - "scenarios a new representation for complex scene understanding"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"
    - "dynamic routing between capsules"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "scene graph generation from objects phrases and region captions"
    - "natural language guided visual relationship detection"

Connected through hyperedge HE-6 (induced by source paper "visual inertial object detection and mapping"):
  with members:
    - "open3d a modern library for 3d data processing"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "faster r cnn towards real time object detection with region proposal 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)