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 "unsupervised learning framework of interest point via properties optimization"):
  Abstract: This paper presents an entirely unsupervised interest point training framework by jointly learning detector and descriptor, which takes an image as input and outputs a probability and a description for every image point. The objective of the training framework is formulated as joint probability distribution of the properties of the extracted points. The essential properties are selected as sparsity, repeatability and discriminability which are formulated by the probabilities. To maximize the objective efficiently, latent variable is introduced to represent the probability of that a point satisfies the required properties. Therefore, original maximization can be optimized with Expectation Maximization algorithm (EM). Considering high computation cost of EM on large scale image set, we implement the optimization process with an efficient strategy as Mini-Batch approximation of EM (MBEM). In the experiments both detector and descriptor are instantiated with fully convolutional network which is named as Property Network (PN). The experiments demonstrate that PN outperforms state-of-the-art methods on a number of image matching benchmarks without need of retraining. PN also reveals that the proposed training framework has high flexibility to adapt to diverse types of scenes.

Includes members:
  Member "lf net learning local features from images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "sips succinct interest points from unsupervised inlierness probability learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description")
  Member "sips unsupervised succinct interest points":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description")
  Member "matching features without descriptors implicitly matched interest points imips":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description")
  Member "superpoint self supervised interest point detection and description":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "hpatches a benchmark and evaluation of handcrafted and learned local descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "lift learned invariant feature transform":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "harmonic networks deep translation and rotation equivariance":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on learning sets of symmetric elements")

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)