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 "repeatability is not enough learning affine regions via discriminability"):
  Abstract: A method for learning local affine-covariant regions is presented. We show that maximizing geometric repeatability does not lead to local regions, a.k.a features,that are reliably matched and this necessitates descriptor-based learning. We explore factors that influence such learning and registration: the loss function, descriptor type, geometric parametrization and the trade-off between matchability and geometric accuracy and propose a novel hard negative-constant loss function for learning of affine regions. The affine shape estimator -- AffNet -- trained with the hard negative-constant loss outperforms the state-of-the-art in bag-of-words image retrieval and wide baseline stereo. The proposed training process does not require precisely geometrically aligned patches.The source codes and trained weights are available at this https URL

Includes members:
  Member "revisiting oxford and paris large scale image retrieval benchmarking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unifying deep local and global features for image search")
  Member "benchmarking 6dof urban visual localization in changing conditions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "real time kinematic ground truth for the oxford robotcar dataset")
  Member "fine tuning cnn image retrieval with no human annotation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  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 "hpatches a benchmark and evaluation of handcrafted and learned local descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "radially distorted conjugate translations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "minimal solvers for indoor uav positioning")
  Member "working hard to know your neighbor s margins local descriptor learning loss":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "systematic evaluation of convolution neural network advances on the imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis")

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)