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: "dynamic body vslam with semantic constraints"
  Abstract: Image based reconstruction of urban environments is a challenging problem that deals with optimization of large number of variables, and has several sources of errors like the presence of dynamic objects. Since most large scale approaches make the assumption of observing static scenes, dynamic objects are relegated to the noise modeling section of such systems. This is an approach of convenience since the RANSAC based framework used to compute most multiview geometric quantities for static scenes naturally confine dynamic objects to the class of outlier measurements. However, reconstructing dynamic objects along with the static environment helps us get a complete picture of an urban environment. Such understanding can then be used for important robotic tasks like path planning for autonomous navigation, obstacle tracking and avoidance, and other areas. In this paper, we propose a system for robust SLAM that works in both static and dynamic environments. To overcome the challenge of dynamic objects in the scene, we propose a new model to incorporate semantic constraints into the reconstruction algorithm. While some of these constraints are based on multi-layered dense CRFs trained over appearance as well as motion cues, other proposed constraints can be expressed as additional terms in the bundle adjustment optimization process that does iterative refinement of 3D structure and camera / object motion trajectories. We show results on the challenging KITTI urban dataset for accuracy of motion segmentation and reconstruction of the trajectory and shape of moving objects relative to ground truth. We are able to show average relative error reduction by a significant amount for moving object trajectory reconstruction relative to state-of-the-art methods like VISO 2, as well as standard bundle adjustment algorithms.

Connected through hyperedge HE-1 (induced by source paper "towards accurate vehicle behaviour classification with multi relational graph convolutional networks"):
  with members:
    - "dynamic traffic scene classification with space time coherence"
    - "learning category specific mesh reconstruction from image collections"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "attention is all you need"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "mask r cnn"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "learning features by watching objects move"

Connected through hyperedge HE-2 (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"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "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"

Connected through hyperedge HE-3 (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"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "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"

Connected through hyperedge HE-4 (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"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "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"

Connected through hyperedge HE-5 (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-6 (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-7 (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-8 (induced by source paper "utilizing semantic visual landmarks for precise vehicle navigation"):
  with members:
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "convolutional neural networks with low rank regularization"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "orb slam a versatile and accurate monocular slam system"

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)