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: "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"
  Abstract: Camera pose estimation is an important problem in computer vision. Common techniques either match the current image against keyframes with known poses, directly regress the pose, or establish correspondences between keypoints in the image and points in the scene to estimate the pose. In recent years, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but have traditionally needed to be trained offline on the target scene, preventing relocalisation in new environments. Recently, we showed how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time. In this paper, we present an extension of this work that achieves significantly better relocalisation performance whilst running fully in real time. To achieve this, we make several changes to the original approach: (i) instead of accepting the camera pose hypothesis without question, we make it possible to score the final few hypotheses using a geometric approach and select the most promising; (ii) we chain several instantiations of our relocaliser together in a cascade, allowing us to try faster but less accurate relocalisation first, only falling back to slower, more accurate relocalisation as necessary; and (iii) we tune the parameters of our cascade to achieve effective overall performance. These changes allow us to significantly improve upon the performance our original state-of-the-art method was able to achieve on the well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional contributions, we present a way of visualising the internal behaviour of our forests and show how to entirely circumvent the need to pre-train a forest on a generic scene.

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"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "end to end cad model retrieval and 9dof alignment in 3d scans"

Connected through hyperedge HE-2 (induced by source paper "hierarchical joint scene coordinate classification and regression for visual localization"):
  with members:
    - "fine grained segmentation networks self supervised segmentation for improved long term visual localization"
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "expert sample consensus applied to camera re localization"
    - "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"
    - "cascaded parallel filtering for memory efficient image based localization"
    - "lcr net multi person 2d and 3d pose detection in natural images"

Connected through hyperedge HE-3 (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"
    - "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"
    - "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"

Connected through hyperedge HE-4 (induced by source paper "dpod 6d pose object detector and refiner"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep model based 6d pose refinement in rgb"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "densepose dense human pose estimation in the wild"
    - "ipose instance aware 6d pose estimation of partly occluded objects"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"

Connected through hyperedge HE-5 (induced by source paper "decoupling features and coordinates for few shot rgb relocalization"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "full frame scene coordinate regression for image based localization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "prototypical networks for few shot learning"
    - "learning to learn by gradient descent by gradient descent"
    - "efficient non consecutive feature tracking for robust structure from motion"
    - "matching networks for one shot learning"
    - "dsac differentiable ransac for camera localization"

Connected through hyperedge HE-6 (induced by source paper "real time highly accurate dense depth on a power budget using an fpga cpu hybrid soc"):
  with members:
    - "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"
    - "real time dense stereo matching with elas on fpga accelerated embedded devices"
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "embedded real time stereo estimation via semi global matching on the gpu"
    - "end to end learning of cost volume aggregation for real time dense stereo"

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)