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: "keyframe based monocular slam"
  Abstract: Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery. A general guideline for the design of a Keyframe-based monocular SLAM.A survey on all Keyframe-based monocular SLAM (KSLAM) systemsInsight into the current state of KSLAM and future directions

Connected through hyperedge HE-1 (induced by source paper "plug and play slam a unified slam architecture for modularity and ease of use"):
  with members:
    - "least squares optimization from theory to practice"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "proslam graph slam from a programmer s perspective"
    - "hbst a hamming distance embedding binary search tree for feature based visual place recognition"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "visual inertial monocular slam with map reuse"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

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 "movable object aware visual slam via weakly supervised semantic segmentation"):
  with members:
    - "a unified framework for mutual improvement of slam and semantic segmentation"
    - "fully using classifiers for weakly supervised semantic segmentation with modified cues"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "ds slam a semantic visual slam towards dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "icnet for real time semantic segmentation on high resolution images"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "a unified formulation for visual odometry"):
  with members:
    - "loosely coupled semi direct monocular slam"
    - "fdmo feature assisted direct monocular odometry"
    - "ldso direct sparse odometry with loop closure"
    - "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect"
    - "direct sparse odometry"
    - "a photometrically calibrated benchmark for monocular visual odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "fdmo feature assisted direct monocular odometry"):
  with members:
    - "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect"
    - "direct sparse odometry"
    - "a photometrically calibrated benchmark for monocular visual odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-6 (induced by source paper "image based camera localization an overview"):
  with members:
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "event based 6 dof camera tracking from photometric depth maps"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "geometric loss functions for camera pose regression with deep learning"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "robust stereo visual inertial odometry for fast autonomous flight"

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)