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 "dual principal component pursuit"):
  Abstract: We consider the problem of outlier rejection in single subspace learning. Classical approaches work directly with a low-dimensional representation of the subspace. Our approach works with a dual representation of the subspace and hence aims to find its orthogonal complement. We pose this problem as an $\ell_1$-minimization problem on the sphere and show that, under certain conditions on the distribution of the data, any global minimizer of this non-convex problem gives a vector orthogonal to the subspace. Moreover, we show that such a vector can still be found by relaxing the non-convex problem with a sequence of linear programs. Experiments on synthetic and real data show that the proposed approach, which we call Dual Principal Component Pursuit (DPCP), outperforms state-of-the art methods, especially in the case of high-dimensional subspaces.

Includes members:
  Member "fast robust and non convex subspace recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "consensus based optimization on the sphere ii convergence to global minimizers and machine learning")
  Member "an overview of robust subspace recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "complete dictionary recovery over the sphere i overview and the geometric picture":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "hyperplane clustering via dual principal component pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "coherence pursuit fast simple and robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inverse transform autoencoder for anomaly detection")
  Member "provable self representation based outlier detection in a union of subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection")
  Member "complete dictionary recovery over the sphere ii recovery by riemannian trust region method":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "finding a sparse vector in a subspace linear sparsity using alternating directions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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)