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: "dynaslam tracking mapping and inpainting in dynamic scenes"
  Abstract: The assumption of scene rigidity is typical in SLAM algorithms. Such a strong assumption limits the use of most visual SLAM systems in populated real-world environments, which are the target of several relevant applications like service robotics or autonomous vehicles. In this letter we present DynaSLAM, a visual SLAM system that, building on ORB-SLAM2, adds the capabilities of dynamic object detection and background inpainting. DynaSLAM is robust in dynamic scenarios for monocular, stereo, and RGB-D configurations. We are capable of detecting the moving objects either by multiview geometry, deep learning, or both. Having a static map of the scene allows inpainting the frame background that has been occluded by such dynamic objects. We evaluate our system in public monocular, stereo, and RGB-D datasets. We study the impact of several accuracy/speed trade-offs to assess the limits of the proposed methodology. DynaSLAM outperforms the accuracy of standard visual SLAM baselines in highly dynamic scenarios. And it also estimates a map of the static parts of the scene, which is a must for long-term applications in real-world environments.

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 "learning to move with affordance maps"):
  with members:
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "learning exploration policies for navigation"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "driving policy transfer via modularity and abstraction"

Connected through hyperedge HE-3 (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"
    - "multimotion visual odometry mvo simultaneous estimation of camera and third party motions"
    - "mid fusion octree based object level multi instance dynamic slam"

Connected through hyperedge HE-4 (induced by source paper "dynamic slam the need for speed"):
  with members:
    - "deep robust single image depth estimation neural network using scene understanding"
    - "cubeslam monocular 3 d object slam"
    - "camera based vehicle velocity estimation from monocular video"
    - "multimotion visual odometry mvo simultaneous estimation of camera and third party motions"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "mask r cnn"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "mot16 a benchmark for multi object tracking"

Connected through hyperedge HE-5 (induced by source paper "rgb d odometry and slam"):
  with members:
    - "cam convs camera aware multi scale convolutions for single view depth"
    - "direct sparse mapping"
    - "deeptam deep tracking and mapping"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "planematch patch coplanarity prediction for robust rgb d reconstruction"
    - "rgbid slam for accurate real time localisation and 3d mapping"
    - "a micro lie theory for state estimation in robotics"

Connected through hyperedge HE-6 (induced by source paper "simvodis simultaneous visual odometry object detection and instance segmentation"):
  with members:
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "fusion volumetric object level slam"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "ds slam a semantic visual slam towards dynamic environments"
    - "digging into self supervised monocular depth estimation"
    - "depth prediction without the sensors leveraging structure for unsupervised learning from monocular videos"

Connected through hyperedge HE-7 (induced by source paper "cubeslam monocular 3 d object slam"):
  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"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"

Connected through hyperedge HE-8 (induced by source paper "a unified framework for mutual improvement of slam and semantic segmentation"):
  with members:
    - "robust dense mapping for large scale dynamic environments"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"

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)