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 "achieving stable subspace clustering by post processing generic clustering results"):
  Abstract: We propose an effective subspace selection scheme as a post-processing step to improve results obtained by sparse subspace clustering (SSC). Our method starts by the computation of stable subspaces using a novel random sampling scheme. Thus constructed preliminary subspaces are used to identify the initially incorrectly clustered data points and then to reassign them to more suitable clusters based on their goodness-of-fit to the preliminary model. To improve the robustness of the algorithm, we use a dominant nearest subspace classification scheme that controls the level of sensitivity against reassignment. We demonstrate that our algorithm is convergent and superior to the direct application of a generic alternative such as principal component analysis. On several popular datasets for motion segmentation and face clustering pervasively used in the sparse subspace clustering literature the proposed method is shown to reduce greatly the incidence of clustering errors while introducing negligible disturbance to the data points already correctly clustered.

Includes members:
  Member "a framework for improving the performance of verification algorithms with a low false positive rate requirement and limited training data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "descriptor transition tables for object retrieval using unconstrained cluttered video acquired using a consumer level handheld mobile device")
  Member "hallucinating optimal high dimensional subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learnt quasi transitive similarity for retrieval from large collections of faces")
  Member "discriminative extended canonical correlation analysis for pattern set matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "descriptor transition tables for object retrieval using unconstrained cluttered video acquired using a consumer level handheld mobile device")
  Member "sparse subspace clustering algorithm theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "a geometric analysis of subspace clustering with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection")
  Member "contextually learnt detection of unusual motion based behaviour in crowded public spaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "track everything limiting prior knowledge in online multi object recognition")
  Member "robust pca via outlier pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "robust principal component analysis":
    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)