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: "pop up slam semantic monocular plane slam for low texture environments"
  Abstract: Existing simultaneous localization and mapping (SLAM) algorithms are not robust in challenging low-texture environments because there are only few salient features. The resulting sparse or semi-dense map also conveys little information for motion planning. Though some work utilize plane or scene layout for dense map regularization, they require decent state estimation from other sources. In this paper, we propose real-time monocular plane SLAM to demonstrate that scene understanding could improve both state estimation and dense mapping especially in low-texture environments. The plane measurements come from a pop-up 3D plane model applied to each single image. We also combine planes with point based SLAM to improve robustness. On a public TUM dataset, our algorithm generates a dense semantic 3D model with pixel depth error of 6.2 cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, our method creates a much better 3D model with state estimation error of 0.67%.

Connected through hyperedge HE-1 (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-2 (induced by source paper "pan tilt zoom slam for sports videos"):
  with members:
    - "panoptic segmentation"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"
    - "sports camera calibration via synthetic data"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "a two point method for ptz camera calibration in sports"
    - "soccer on your tabletop"
    - "light cascaded convolutional neural networks for accurate player detection"

Connected through hyperedge HE-3 (induced by source paper "ucoslam simultaneous localization and mapping by fusion of keypoints and squared planar markers"):
  with members:
    - "ldso direct sparse odometry with loop closure"
    - "artificial intelligence for long term robot autonomy a survey"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mapping and localization from planar markers"
    - "direct sparse odometry"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "an open source fiducial based visual inertial state estimation system"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-4 (induced by source paper "oriented point sampling for plane detection in unorganized point clouds"):
  with members:
    - "plane extraction from depth data using a gaussian mixture regression model"
    - "de noising stabilizing and completing 3d reconstructions on the go using plane priors"

Connected through hyperedge HE-5 (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"
    - "meaningful maps with object oriented semantic mapping"

Connected through hyperedge HE-6 (induced by source paper "monocular object and plane slam in structured environments"):
  with members:
    - "cubeslam monocular 3 d object slam"
    - "fusion volumetric object level slam"
    - "structure aware slam using quadrics and planes"
    - "cubeslam monocular 3d object detection and slam without prior models"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "joint layout estimation and global multi view registration for indoor reconstruction"
    - "semantic 3d occupancy mapping through efficient high order crfs"

Connected through hyperedge HE-7 (induced by source paper "understanding convolutional networks with apple automatic patch pattern labeling for explanation"):
  with members:
    - "context aware captions from context agnostic supervision"
    - "attentive explanations justifying decisions and pointing to the evidence"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-8 (induced by source paper "real time monocular object model aware sparse slam"):
  with members:
    - "structure aware slam using quadrics and planes"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "yolov3 an incremental improvement"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "dual quadrics from object detection boundingboxes as landmark representations in slam"
    - "meaningful maps with object oriented semantic mapping"

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)