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: "matchable image retrieval by learning from surface reconstruction"
  Abstract: Convolutional Neural Networks (CNNs) have achieved superior performance on object image retrieval, while Bag-of-Words (BoW) models with handcrafted local features still dominate the retrieval of overlapping images in 3D reconstruction. In this paper, we narrow down this gap by presenting an efficient CNN-based method to retrieve images with overlaps, which we refer to as the matchable image retrieval problem. Different from previous methods that generates training data based on sparse reconstruction, we create a large-scale image database with rich 3D geometrics and exploit information from surface reconstruction to obtain fine-grained training data. We propose a batched triplet-based loss function combined with mesh re-projection to effectively learn the CNN representation. The proposed method significantly accelerates the image retrieval process in 3D reconstruction and outperforms the state-of-the-art CNN-based and BoW methods for matchable image retrieval. The code and data are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "contextdesc local descriptor augmentation with cross modality context"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "fix your classifier the marginal value of training the last weight layer"
    - "local descriptors optimized for average precision"
    - "learning and matching multi view descriptors for registration of point clouds"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "repeatability is not enough learning affine regions via discriminability"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-2 (induced by source paper "beyond photometric loss for self supervised ego motion estimation"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "ba net dense bundle adjustment network"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"

Connected through hyperedge HE-3 (induced by source paper "kornia an open source differentiable computer vision library for pytorch"):
  with members:
    - "chainer a deep learning framework for accelerating the research cycle"
    - "from handcrafted to deep local invariant features"
    - "universal perturbation attack against image retrieval"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "structure from motion using dense cnn features with keypoint relocalization"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-4 (induced by source paper "leveraging outdoor webcams for local descriptor learning"):
  with members:
    - "from handcrafted to deep local invariant features"
    - "local descriptors optimized for average precision"
    - "learning a local feature descriptor for 3d lidar scans"
    - "structure from motion using dense cnn features with keypoint relocalization"
    - "a large dataset for improving patch matching"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "an analysis of deep neural network models for practical applications"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"

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)