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: "edge slam edge points based monocular visual slam"
  Abstract: Visual SLAM shows significant progress in recent years due to high attention from vision community but still, challenges remain for low-textured environments. Feature based visual SLAMs do not produce reliable camera and structure estimates due to insufficient features in a low-textured environment. Moreover, existing visual SLAMs produce partial reconstruction when the number of 3D-2D correspondences is insufficient for incremental camera estimation using bundle adjustment. This paper presents Edge SLAM, a feature based monocular visual SLAM which mitigates the above mentioned problems. Our proposed Edge SLAM pipeline detects edge points from images and tracks those using optical flow for point correspondence. We further refine these point correspondences using geometrical relationship among three views. Owing to our edge-point tracking, we use a robust method for two-view initialization for bundle adjustment. Our proposed SLAM also identifies the potential situations where estimating a new camera into the existing reconstruction is becoming unreliable and we adopt a novel method to estimate the new camera reliably using a local optimization technique. We present an extensive evaluation of our proposed SLAM pipeline with most popular open datasets and compare with the state-of-the art. Experimental result indicates that our Edge SLAM is robust and works reliably well for both textured and less-textured environment in comparison to existing state-of-the-art SLAMs.

Connected through hyperedge HE-1 (induced by source paper "robust monocular edge visual odometry through coarse to fine data association"):
  with members:
    - "semantic nearest neighbor fields monocular edge visual odometry"
    - "se slam semi dense structured edge based monocular slam"
    - "ldso direct sparse odometry with loop closure"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "semi dense visual odometry for rgb d cameras using approximate nearest neighbour fields"
    - "direct sparse odometry"
    - "holistically nested edge detection"

Connected through hyperedge HE-2 (induced by source paper "integrating objects into monocular slam line based category specific models"):
  with members:
    - "constructing category specific models for monocular object slam"
    - "cubeslam monocular 3d object detection and slam without prior models"
    - "slam with objects using a nonparametric pose graph"
    - "object centric photometric bundle adjustment with deep shape prior"
    - "stacked hourglass networks for human pose estimation"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-3 (induced by source paper "se slam semi dense structured edge based monocular slam"):
  with members:
    - "ldso direct sparse odometry with loop closure"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "direct monocular odometry using points and lines"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "pl slam a stereo slam system through the combination of points and line segments"
    - "from monocular slam to autonomous drone exploration"
    - "direct sparse odometry"

Connected through hyperedge HE-4 (induced by source paper "robust semi direct monocular visual odometry using edge and illumination robust cost"):
  with members:
    - "semantic nearest neighbor fields monocular edge visual odometry"
    - "loosely coupled semi direct monocular slam"
    - "ldso direct sparse odometry with loop closure"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "holistically nested edge detection"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "a hierarchical network for diverse trajectory proposals"):
  with members:
    - "naturalistic driver intention and path prediction using recurrent neural networks"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "benchmarking motion planning algorithms an extensible infrastructure for analysis and visualization"
    - "end to end training of deep visuomotor policies"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (induced by source paper "edge direct visual odometry"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "direct sparse odometry"
    - "holistically nested edge detection"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-7 (induced by source paper "indoor dense depth map at drone hovering"):
  with members:
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-8 (induced by source paper "edge detection for event cameras using intra pixel area events"):
  with members:
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"

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)