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 "neighbourhood consensus networks"):
  Abstract: We address the problem of finding reliable dense correspondences between a pair of images. This is a challenging task due to strong appearance differences between the corresponding scene elements and ambiguities generated by repetitive patterns. The contributions of this work are threefold. First, inspired by the classic idea of disambiguating feature matches using semi-local constraints, we develop an end-to-end trainable convolutional neural network architecture that identifies sets of spatially consistent matches by analyzing neighbourhood consensus patterns in the 4D space of all possible correspondences between a pair of images without the need for a global geometric model. Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences. Third, we show the proposed neighbourhood consensus network can be applied to a range of matching tasks including both category- and instance-level matching, obtaining the state-of-the-art results on the PF Pascal dataset and the InLoc indoor visual localization benchmark.

Includes members:
  Member "inloc indoor visual localization with dense matching and view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  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 "end to end weakly supervised semantic alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "os2d one stage one shot object detection by matching anchor features")
  Member "matching neural paths transfer from recognition to correspondence search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "r2d2 repeatable and reliable detector and descriptor")
  Member "convolutional neural network architecture for geometric matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "os2d one stage one shot object detection by matching anchor features")
  Member "scnet learning semantic correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a two stream symmetric network with bidirectional ensemble for aerial image matching")
  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 "pn net conjoined triple deep network for learning local image descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "contextdesc local descriptor augmentation with cross modality context")

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)