human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "local descriptors optimized for average precision":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "large scale evaluation of local image feature detectors on homography datasets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from handcrafted to deep local features")
  Member "superpoint self supervised interest point detection and description":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "learning spread out local feature descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  Member "lift learned invariant feature transform":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "pn net conjoined triple deep network for learning local image descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "contextdesc local descriptor augmentation with cross modality context")
  Member "learning deep embeddings with histogram loss":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")

Question: Please tell me which class the source paper 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)