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: "spp net deep absolute pose regression with synthetic views"
  Abstract: Image based localization is one of the important problems in computer vision due to its wide applicability in robotics, augmented reality, and autonomous systems. There is a rich set of methods described in the literature how to geometrically register a 2D image w.r.t.\ a 3D model. Recently, methods based on deep (and convolutional) feedforward networks (CNNs) became popular for pose regression. However, these CNN-based methods are still less accurate than geometry based methods despite being fast and memory efficient. In this work we design a deep neural network architecture based on sparse feature descriptors to estimate the absolute pose of an image. Our choice of using sparse feature descriptors has two major advantages: first, our network is significantly smaller than the CNNs proposed in the literature for this task---thereby making our approach more efficient and scalable. Second---and more importantly---, usage of sparse features allows to augment the training data with synthetic viewpoints, which leads to substantial improvements in the generalization performance to unseen poses. Thus, our proposed method aims to combine the best of the two worlds---feature-based localization and CNN-based pose regression--to achieve state-of-the-art performance in the absolute pose estimation. A detailed analysis of the proposed architecture and a rigorous evaluation on the existing datasets are provided to support our method.

Connected through hyperedge HE-1 (induced by source paper "comparison network for one shot conditional object detection"):
  with members:
    - "meta learning with latent embedding optimization"
    - "zero shot object detection by hybrid region embedding"
    - "metalearning with hebbian fast weights"
    - "large margin few shot learning"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "improving image based localization with deep learning the impact of the loss function"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "full frame scene coordinate regression for image based localization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "image based localization using hourglass networks"
    - "relative camera pose estimation using convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "rpnet an end to end network for relative camera pose estimation"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "superpoint self supervised interest point detection and description"
    - "convolutional neural network architecture for geometric matching"
    - "relative camera pose estimation using convolutional neural networks"
    - "learning to find good correspondences"
    - "lift learned invariant feature transform"
    - "image based localization using lstms for structured feature correlation"
    - "dsac differentiable ransac for camera localization"

Connected through hyperedge HE-4 (induced by source paper "pointflownet learning representations for rigid motion estimation from point clouds"):
  with members:
    - "flex convolution million scale point cloud learning beyond grid worlds"
    - "splatnet sparse lattice networks for point cloud processing"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "a continuous optimization approach for efficient and accurate scene flow"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-5 (induced by source paper "hinted networks"):
  with members:
    - "deep convolutional neural network for 6 dof image localization"

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)