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: "ldso direct sparse odometry with loop closure"
  Abstract: In this paper we present an extension of Direct Sparse Odometry (DSO) to a monocular visual SLAM system with loop closure detection and pose-graph optimization (LDSO). As a direct technique, DSO can utilize any image pixel with sufficient intensity gradient, which makes it robust even in featureless areas. LDSO retains this robustness, while at the same time ensuring repeatability of some of these points by favoring corner features in the tracking frontend. This repeatability allows to reliably detect loop closure candidates with a conventional feature-based bag-of-words (BoW) approach. Loop closure candidates are verified geometrically and Sim(3) relative pose constraints are estimated by jointly minimizing 2D and 3D geometric error terms. These constraints are fused with a co-visibility graph of relative poses extracted from DSO's sliding window optimization. Our evaluation on publicly available datasets demonstrates that the modified point selection strategy retains the tracking accuracy and robustness, and the integrated pose-graph optimization significantly reduces the accumulated rotation-, translation- and scale-drift, resulting in an overall performance comparable to state-of-the-art feature-based systems, even without global bundle adjustment.

Connected through hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "learning monocular depth estimation infusing traditional stereo knowledge"
    - "deep direct visual odometry"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "multi frame gan image enhancement for stereo visual odometry in low light"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "sganvo unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks"

Connected through hyperedge HE-2 (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"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "edge slam edge points based monocular visual slam"
    - "semi dense visual odometry for rgb d cameras using approximate nearest neighbour fields"
    - "direct sparse odometry"
    - "holistically nested edge detection"

Connected through hyperedge HE-3 (induced by source paper "intensity scan context coding intensity and geometry relations for loop closure detection"):
  with members:
    - "night to day image translation for retrieval based localization"
    - "local descriptor for robust place recognition using lidar intensity"
    - "non iterative slam"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "segmatch segment based place recognition in 3d point clouds"

Connected through hyperedge HE-4 (induced by source paper "deep direct visual odometry"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "a unified framework for mutual improvement of slam and semantic segmentation"
    - "dsvo direct stereo visual odometry"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "cnn svo improving the mapping in semi direct visual odometry using single image depth prediction"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "direct sparse visual inertial odometry using dynamic marginalization"

Connected through hyperedge HE-5 (induced by source paper "gslam a general slam framework and benchmark"):
  with members:
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "high quality monocular depth estimation via transfer learning"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "topometric localization with deep learning"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-6 (induced by source paper "directshape photometric alignment of shape priors for visual vehicle pose and shape estimation"):
  with members:
    - "panoptic segmentation"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pseudo lidar from visual depth estimation bridging the gap in 3d object detection for autonomous driving"
    - "pyramid stereo matching network"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "deformable convolutional networks"
    - "object centric photometric bundle adjustment with deep shape prior"

Connected through hyperedge HE-7 (induced by source paper "retrieval based localization based on domain invariant feature learning under changing environments"):
  with members:
    - "a multi domain feature learning method for visual place recognition"
    - "multimodal unsupervised image to image translation"
    - "night to day image translation for retrieval based localization"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "combogan unrestrained scalability for image domain translation"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-8 (induced by source paper "low latency visual slam with appearance enhanced local map building"):
  with members:
    - "on device scalable image based localization via prioritized cascade search and fast one many ransac"
    - "good feature selection for least squares pose optimization in vo vslam"
    - "appearance based landmark selection for efficient long term visual localization"
    - "experiments in fast autonomous gps denied quadrotor flight"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "visual inertial monocular slam with map reuse"
    - "direct sparse odometry"
    - "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)