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: "self improving visual odometry"
  Abstract: We propose a self-supervised learning framework that uses unlabeled monocular video sequences to generate large-scale supervision for training a Visual Odometry (VO) frontend, a network which computes pointwise data associations across images. Our self-improving method enables a VO frontend to learn over time, unlike other VO and SLAM systems which require time-consuming hand-tuning or expensive data collection to adapt to new environments. Our proposed frontend operates on monocular images and consists of a single multi-task convolutional neural network which outputs 2D keypoints locations, keypoint descriptors, and a novel point stability score. We use the output of VO to create a self-supervised dataset of point correspondences to retrain the frontend. When trained using VO at scale on 2.5 million monocular images from ScanNet, the stability classifier automatically discovers a ranking for keypoints that are not likely to help in VO, such as t-junctions across depth discontinuities, features on shadows and highlights, and dynamic objects like people. The resulting frontend outperforms both traditional methods (SIFT, ORB, AKAZE) and deep learning methods (SuperPoint and LF-Net) in a 3D-to-2D pose estimation task on ScanNet.

Connected through hyperedge HE-1 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry"):
  with members:
    - "visual inertial mapping with non linear factor recovery"
    - "panoptic segmentation"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "multi frame gan image enhancement for stereo visual odometry in low light"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "visual odometry revisited what should be learnt"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"

Connected through hyperedge HE-2 (induced by source paper "self supervised 3d keypoint learning for ego motion estimation"):
  with members:
    - "two stream networks for self supervised ego motion estimation"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "unsuperpoint end to end unsupervised interest point detector and descriptor"
    - "deep high resolution representation learning for human pose estimation"
    - "fcos fully convolutional one stage object detection"
    - "visual odometry revisited what should be learnt"
    - "neural outlier rejection for self supervised keypoint learning"

Connected through hyperedge HE-3 (induced by source paper "superglue learning feature matching with graph neural networks"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "dual graph convolutional network for semantic segmentation"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "neural guided ransac learning where to sample model hypotheses"
    - "graph matching networks for learning the similarity of graph structured objects"
    - "deep closest point learning representations for point cloud registration"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-4 (induced by source paper "learning two view correspondences and geometry using order aware network"):
  with members:
    - "contextdesc local descriptor augmentation with cross modality context"
    - "efficient semantic scene completion network with spatial group convolution"
    - "lf net learning local features from images"
    - "hierarchical graph representation learning with differentiable pooling"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "neural nearest neighbors networks"
    - "learning and matching multi view descriptors for registration of point clouds"
    - "geodesc learning local descriptors by integrating geometry constraints"

Connected through hyperedge HE-5 (induced by source paper "neural outlier rejection for self supervised keypoint learning"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "3d packing for self supervised monocular depth estimation"
    - "unsuperpoint end to end unsupervised interest point detector and descriptor"
    - "deep high resolution representation learning for human pose estimation"
    - "fcos fully convolutional one stage object detection"
    - "panoptic feature pyramid networks"
    - "revisiting self supervised visual representation learning"
    - "lf net learning local features from images"

Connected through hyperedge HE-6 (induced by source paper "learning local feature descriptor with motion attribute for vision based localization"):
  with members:
    - "lf net learning local features from images"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "a large dataset for improving patch matching"
    - "learning from large scale noisy web data with ubiquitous reweighting for image classification"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "superpoint self supervised interest point detection and description"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

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)