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: "learning less is more 6d camera localization via 3d surface regression"
  Abstract: Popular research areas like autonomous driving and augmented reality have renewed the interest in image-based camera localization. In this work, we address the task of predicting the 6D camera pose from a single RGB image in a given 3D environment. With the advent of neural networks, previous works have either learned the entire camera localization process, or multiple components of a camera localization pipeline. Our key contribution is to demonstrate and explain that learning a single component of this pipeline is sufficient. This component is a fully convolutional neural network for densely regressing so-called scene coordinates, defining the correspondence between the input image and the 3D scene space. The neural network is prepended to a new end-to-end trainable pipeline. Our system is efficient, highly accurate, robust in training, and exhibits outstanding generalization capabilities. It exceeds state-of-the-art consistently on indoor and outdoor datasets. Interestingly, our approach surpasses existing techniques even without utilizing a 3D model of the scene during training, since the network is able to discover 3D scene geometry automatically, solely from single-view constraints.

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 "rsl net localising in satellite images from a radar on the ground"):
  with members:
    - "masking by moving learning distraction free radar odometry from pose information"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "conditional image to image translation"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "learning steerable filters for rotation equivariant cnns"
    - "polar transformer networks"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-3 (induced by source paper "consac robust multi model fitting by conditional sample consensus"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "progressive x efficient anytime multi model fitting algorithm"
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "multi class model fitting by energy minimization and mode seeking"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "learning to find good correspondences"

Connected through hyperedge HE-4 (induced by source paper "relative visual localization for unmanned aerial systems"):
  with members:
    - "3d pose regression using convolutional neural networks"

Connected through hyperedge HE-5 (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-6 (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-7 (induced by source paper "cross view semantic segmentation for sensing surroundings"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "high resolution representations for labeling pixels and regions"
    - "learning exploration policies for navigation"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "learning to adapt structured output space for semantic segmentation"
    - "cross view image synthesis using conditional gans"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "visual memory for robust path following"

Connected through hyperedge HE-8 (induced by source paper "introduction to camera pose estimation with deep learning"):
  with members:
    - "learning metrics from teachers compact networks for image embedding"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "learning loss for active learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "do better imagenet models transfer better"
    - "deep global relative networks for end to end 6 dof visual localization and odometry"
    - "deep auxiliary learning for visual localization and odometry"

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)