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: "robust dense mapping for large scale dynamic environments"
  Abstract: We present a stereo-based dense mapping algorithm for large-scale dynamic urban environments. In contrast to other existing methods, we simultaneously reconstruct the static background, the moving objects, and the potentially moving but currently stationary objects separately, which is desirable for high-level mobile robotic tasks such as path planning in crowded environments. We use both instance-aware semantic segmentation and sparse scene flow to classify objects as either background, moving, or potentially moving, thereby ensuring that the system is able to model objects with the potential to transition from static to dynamic, such as parked cars. Given camera poses estimated from visual odometry, both the background and the (potentially) moving objects are reconstructed separately by fusing the depth maps computed from the stereo input. In addition to visual odometry, sparse scene flow is also used to estimate the 3D motions of the detected moving objects, in order to reconstruct them accurately. A map pruning technique is further developed to improve reconstruction accuracy and reduce memory consumption, leading to increased scalability. We evaluate our system thoroughly on the well-known KITTI dataset. Our system is capable of running on a PC at approximately 2.5Hz, with the primary bottleneck being the instance-aware semantic segmentation, which is a limitation we hope to address in future work. The source code is available from the project websiteaahttp://andreibarsan.github.io/dynslam.

Connected through hyperedge HE-1 (induced by source paper "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "spatio temporal action graph networks"
    - "learning actor relation graphs for group activity recognition"
    - "intent aware multi agent reinforcement learning"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "classifying collisions with spatio temporal action graph networks"
    - "semi supervised learning fusion of self supervised supervised learning and multimodal cues for tactical driver behavior detection"

Connected through hyperedge HE-2 (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"
    - "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"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"

Connected through hyperedge HE-3 (induced by source paper "a unified framework for mutual improvement of slam and semantic segmentation"):
  with members:
    - "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"
    - "pyramid scene parsing network"

Connected through hyperedge HE-4 (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"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "pyramid stereo matching network"
    - "mid fusion octree based object level multi instance dynamic slam"
    - "joint 3d proposal generation and object detection from view aggregation"

Connected through hyperedge HE-5 (induced by source paper "track to reconstruct and reconstruct to track"):
  with members:
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "mots multi object tracking and segmentation"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "combined image and world space tracking in traffic scenes"
    - "mono camera 3d multi object tracking using deep learning detections and pmbm filtering"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "premvos proposal generation refinement and merging for video object segmentation"
    - "learning optimal parameters for multi target tracking with contextual interactions"

Connected through hyperedge HE-6 (induced by source paper "inferring distributions over depth from a single image"):
  with members:
    - "lightweight probabilistic deep networks"
    - "brute force facial landmark analysis with a 140 000 way classifier"
    - "deep ordinal regression network for monocular depth estimation"
    - "on a formal model of safe and scalable self driving cars"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "semi supervised deep learning for monocular depth map prediction"
    - "learning depth from single monocular images using deep convolutional neural fields"

Connected through hyperedge HE-7 (induced by source paper "monocular outdoor semantic mapping with a multi task network"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "unsupervised learning of depth and ego motion from video"
    - "fast scene understanding for autonomous driving"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-8 (induced by source paper "real time scalable dense surfel mapping"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "pyramid stereo matching network"
    - "mvdepthnet real time multiview depth estimation neural network"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "unsupervised monocular depth estimation with left right consistency"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"

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)