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: "on the fly adaptation of regression forests for online camera relocalisation"
  Abstract: Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation. Common techniques either match the current image against keyframes with known poses coming from a tracker, or establish 2D-to-3D correspondences between keypoints in the current image and points in the scene in order to estimate the camera pose. Recently, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but must be trained offline on the target scene, preventing relocalisation in new environments. In this paper, we show how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. Our adapted forests achieve relocalisation performance that is on par with that of offline forests, and our approach runs in under 150ms, making it desirable for real-time systems that require online relocalisation.

Connected through hyperedge HE-1 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "sfnet learning object aware semantic correspondence"
    - "neural guided ransac learning where to sample model hypotheses"
    - "reinforced feature points optimizing feature detection and description for a high level task"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"
    - "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"
    - "r2d2 repeatable and reliable detector and descriptor"

Connected through hyperedge HE-2 (induced by source paper "fine grained segmentation networks self supervised segmentation for improved long term visual localization"):
  with members:
    - "sparse to dense hypercolumn matching for long term visual localization"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "a cross season correspondence dataset for robust semantic segmentation"
    - "deep clustering for unsupervised learning of visual features"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-3 (induced by source paper "to learn or not to learn visual localization from essential matrices"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep auxiliary learning for visual localization and odometry"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "efficient 2d 3d matching for multi camera visual localization"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "neighbourhood consensus networks"

Connected through hyperedge HE-4 (induced by source paper "understanding the limitations of cnn based absolute camera pose regression"):
  with members:
    - "lf net learning local features from images"
    - "deep auxiliary learning for visual localization and odometry"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "neighbourhood consensus networks"
    - "improved visual relocalization by discovering anchor points"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-5 (induced by source paper "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"
    - "relative geometry aware siamese neural network for 6dof camera relocalization"
    - "adversarial joint image and pose distribution learning for camera pose regression and refinement"
    - "deep auxiliary learning for visual localization and odometry"
    - "scene coordinate regression with angle based reprojection loss for camera relocalization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"

Connected through hyperedge HE-6 (induced by source paper "is this the right place geometric semantic pose verification for indoor visual localization"):
  with members:
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "taskonomy disentangling task transfer learning"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "vlase vehicle localization by aggregating semantic edges"
    - "long term visual localization using semantically segmented images"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-7 (induced by source paper "rgb d odometry and slam"):
  with members:
    - "cam convs camera aware multi scale convolutions for single view depth"
    - "direct sparse mapping"
    - "deeptam deep tracking and mapping"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "planematch patch coplanarity prediction for robust rgb d reconstruction"
    - "rgbid slam for accurate real time localisation and 3d mapping"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"

Connected through hyperedge HE-8 (induced by source paper "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "scene coordinate regression with angle based reprojection loss for camera relocalization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"
    - "full frame scene coordinate regression for image based localization"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"

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)