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 "a direction search and spectral clustering based approach to subspace clustering"):
  Abstract: This paper presents a new spectral-clustering-based approach to the subspace clustering problem in which the data lies in the union of an unknown number of unknown linear subspaces. Underpinning the proposed method is a convex program for optimal direction search, which for each data point d, finds an optimal direction in the span of the data that has minimum projection on the other data points and non-vanishing projection on d. The obtained directions are subsequently leveraged to identify a neighborhood set for each data point. An Alternating Direction Method of Multipliers (ADMM) framework is provided to efficiently solve for the optimal directions. The proposed method is shown to often outperform the existing subspace clustering methods, particularly for unwieldy scenarios involving high levels of noise and close subspaces, and yields the state-of-the-art results for the problem of face clustering using subspace segmentation.

Includes members:
  Member "spatial random sampling a structure preserving data sketching tool":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph analysis and graph pooling in the spatial domain")
  Member "innovation pursuit a new approach to subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "minimal sample subspace learning theory and algorithms")
  Member "robust computation of linear models by convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "robust recovery of subspace structures by low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "greedy feature selection for subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "generalized principal component analysis gpca":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "salient object detection a survey")
  Member "sparse subspace clustering algorithm theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection")

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)