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 "synchronisation of partial multi matchings via non negative factorisations"):
  Abstract: Abstract   In this work we study permutation synchronisation for the challenging case of partial permutations, which plays an important role for the problem of matching multiple objects (e.g. images or shapes). The term synchronisation refers to the property that the set of pairwise matchings is cycle-consistent, i.e. in the full matching case all compositions of pairwise matchings over cycles must be equal to the identity. Motivated by clustering and matrix factorisation perspectives of cycle-consistency, we derive an algorithm to tackle the permutation synchronisation problem based on non-negative factorisations. In order to deal with the inherent non-convexity of the permutation synchronisation problem, we use an initialisation procedure based on a novel rotation scheme applied to the solution of the spectral relaxation. Moreover, this rotation scheme facilitates a convenient Euclidean projection to obtain a binary solution after solving our relaxed problem. In contrast to state-of-the-art methods, our approach is guaranteed to produce cycle-consistent results. We experimentally demonstrate the efficacy of our method and show that it achieves better results compared to existing methods.

Includes members:
  Member "ds a flexible scalable and provably tight relaxation for matching problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment")
  Member "distributable consistent multi object matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rgb d individual segmentation")
  Member "a constrained clustering based approach for matching a collection of feature sets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "clear a consistent lifting embedding and alignment rectification algorithm for multi agent data association")
  Member "spectral synchronization of multiple views in se 3":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning multiview 3d point cloud registration")
  Member "efficient and non convex coordinate descent for symmetric nonnegative matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "accelerating nonnegative matrix factorization algorithms using extrapolation")
  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 "a dual ascent framework for lagrangean decomposition of combinatorial problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "map inference via block coordinate frank wolfe algorithm")
  Member "alternating direction graph matching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment")

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)