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 "multi image semantic matching by mining consistent features"):
  Abstract: This work proposes a multi-image matching method to estimate semantic correspondences across multiple images. In contrast to the previous methods that optimize all pairwise correspondences, the proposed method identifies and matches only a sparse set of reliable features in the image collection. In this way, the proposed method is able to prune nonrepeatable features and also highly scalable to handle thousands of images. We additionally propose a low-rank constraint to ensure the geometric consistency of feature correspondences over the whole image collection. Besides the competitive performance on multi-graph matching and semantic flow benchmarks, we also demonstrate the applicability of the proposed method to reconstruct object-class models and discover object-class landmarks from images without any annotation.

Includes members:
  Member "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning correspondence from the cycle consistency of time")
  Member "proposal flow semantic correspondences from object proposals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning semantic correspondence exploiting an object level prior")
  Member "unsupervised learning of object landmarks by factorized spatial embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency")
  Member "lift learned invariant feature transform":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "warpnet weakly supervised matching for single view reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised disentanglement of pose appearance and background from images and videos")
  Member "multi graph matching via affinity optimization with graduated consistency regularization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adapnet adaptability decomposing encoder decoder network for weakly supervised action recognition and localization")
  Member "learning dense correspondence via 3d guided cycle consistency":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sim2real transfer learning for 3d pose estimation motion to the rescue")
  Member "learning covariant feature detectors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "provably scale covariant continuous hierarchical networks based on scale normalized differential expressions coupled in cascade")

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)