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 "clear a consistent lifting embedding and alignment rectification algorithm for multi agent data association"):
  Abstract: Many robotics applications require alignment and fusion of observations obtained at multiple views to form a global model of the environment. Multi-way data association methods provide a mechanism to improve alignment accuracy of pairwise associations and ensure their consistency. However, existing methods that solve this computationally challenging problem are often too slow for real-time applications. Furthermore, some of the existing techniques can violate the cycle consistency principle, thus drastically reducing the fusion accuracy. This work presents the CLEAR (Consistent Lifting, Embedding, and Alignment Rectification) algorithm to address these issues. By leveraging insights from the multi-way matching and spectral graph clustering literature, CLEAR provides cycle consistent and accurate solutions in a computationally efficient manner. Numerical experiments on both synthetic and real datasets are carried out to demonstrate the scalability and superior performance of our algorithm in real-world problems. This algorithmic framework can provide significant improvement in the accuracy and efficiency of existing discrete assignment problems, which traditionally use pairwise (but potentially inconsistent) correspondences. An implementation of CLEAR is made publicly available online.

Includes members:
  Member "synchronisation of partial multi matchings via non negative factorisations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope")
  Member "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3dregnet a deep neural network for 3d point registration")
  Member "multi image semantic matching by mining consistent features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope")
  Member "distributable consistent multi object matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "synchronisation of partial multi matchings via non negative factorisations")
  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 "netvlad cnn architecture for weakly supervised place recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")
  Member "multi image matching via fast alternating minimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")

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.RO(Robotics)