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: "from handcrafted to deep local invariant features"
  Abstract: The aim of this paper is to present a comprehensive overview of the evolution of local features from handcrafted to deep learning based methods, followed by a discussion of several benchmark and evaluation papers about this topic. Our investigations are motivated by 3D reconstruction problems, where the precise location of the features are important. During the description of the methods, we highlight and explain challenges of feature extraction and potential ways to overcome them. We first present traditional handcrafted methods, followed by data driven learning based approaches, and finally detail deep learning based methods. We are convinced that this evolutionary presentation will help the reader to fully understand the topic of image and region description in order to make best use of it in modern computer vision applications. In other words, understanding traditional methods and their motivation will help understanding modern approaches and how machine learning is used to improve the results. We also provide comprehensive references to most relevant literature and code.

Connected through hyperedge HE-1 (induced by source paper "sketchdesc learning local sketch descriptors for multi view correspondence"):
  with members:
    - "sketchzooms deep multi view descriptors for matching line drawings"
    - "hierarchical deep stereo matching on high resolution images"
    - "sosnet second order similarity regularization for local descriptor learning"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "sketch r2cnn an attentive network for vector sketch recognition"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "fast sketch segmentation and labeling with deep learning"
    - "repeatability is not enough learning affine regions via discriminability"

Connected through hyperedge HE-2 (induced by source paper "r2d2 repeatable and reliable detector and descriptor"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lf net learning local features from images"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "local descriptors optimized for average precision"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "inloc indoor visual localization with dense matching and view synthesis"

Connected through hyperedge HE-3 (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"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "leveraging outdoor webcams for local descriptor learning"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "lf net learning local features from images"
    - "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"

Connected through hyperedge HE-4 (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"
    - "matchable image retrieval by learning from surface reconstruction"
    - "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-5 (induced by source paper "leveraging outdoor webcams for local descriptor learning"):
  with members:
    - "matchable image retrieval by learning from surface reconstruction"
    - "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)