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: "contextdesc local descriptor augmentation with cross modality context"
  Abstract: Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keypoint locations. In this paper, we go beyond the local detail representation by introducing context awareness to augment off-the-shelf local feature descriptors. Specifically, we propose a unified learning framework that leverages and aggregates the cross-modality contextual information, including (i) visual context from high-level image representation, and (ii) geometric context from 2D keypoint distribution. Moreover, we propose an effective N-pair loss that eschews the empirical hyper-parameter search and improves the convergence. The proposed augmentation scheme is lightweight compared with the raw local feature description, meanwhile improves remarkably on several large-scale benchmarks with diversified scenes, which demonstrates both strong practicality and generalization ability in geometric matching applications.

Connected through hyperedge HE-1 (induced by source paper "image matching across wide baselines from paper to practice"):
  with members:
    - "self supervised learning of depth and ego motion with differentiable bundle adjustment"
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "explicit spatial encoding for deep local descriptors"
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "neural guided ransac learning where to sample model hypotheses"
    - "nm net mining reliable neighbors for robust feature correspondences"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "adversarial networks for camera pose regression and refinement"

Connected through hyperedge HE-2 (induced by source paper "unifying deep local and global features for image search"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "explicit spatial encoding for deep local descriptors"
    - "local features and visual words emerge in activations"
    - "learning with average precision training image retrieval with a listwise loss"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "lf net learning local features from images"
    - "arcface additive angular margin loss for deep face recognition"
    - "local descriptors optimized for average precision"

Connected through hyperedge HE-3 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network"):
  with members:
    - "fine grained segmentation networks self supervised segmentation for improved long term visual localization"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "deep auxiliary learning for visual localization and odometry"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "geometric consistency for self supervised end to end visual odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "improved visual relocalization by discovering anchor points"
    - "how to train a cat learning canonical appearance transformations for direct visual localization under illumination change"

Connected through hyperedge HE-4 (induced by source paper "d3feat joint learning of dense detection and description of 3d local features"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "kpconv flexible and deformable convolution for point clouds"
    - "unsuperpoint end to end unsupervised interest point detector and descriptor"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "lf net learning local features from images"
    - "open3d a modern library for 3d data processing"

Connected through hyperedge HE-5 (induced by source paper "superglue learning feature matching with graph neural networks"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "dual graph convolutional network for semantic segmentation"
    - "neural guided ransac learning where to sample model hypotheses"
    - "graph matching networks for learning the similarity of graph structured objects"
    - "deep closest point learning representations for point cloud registration"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "set transformer a framework for attention based permutation invariant neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning two view correspondences and geometry using order aware network"):
  with members:
    - "efficient semantic scene completion network with spatial group convolution"
    - "lf net learning local features from images"
    - "hierarchical graph representation learning with differentiable pooling"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "neural nearest neighbors networks"
    - "learning and matching multi view descriptors for registration of point clouds"
    - "self improving visual odometry"
    - "geodesc learning local descriptors by integrating geometry constraints"

Connected through hyperedge HE-7 (induced by source paper "dr kfd a differentiable visual metric for 3d shape reconstruction"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "the perception distortion tradeoff"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"

Connected through hyperedge HE-8 (induced by source paper "beyond cartesian representations for local descriptors"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "leveraging outdoor webcams for local descriptor learning"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "lf net learning local features from images"
    - "from handcrafted to deep local invariant features"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "local descriptors optimized for average precision"
    - "geodesc learning local descriptors by integrating geometry constraints"

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)