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 "subspace clustering by block diagonal representation"):
  Abstract: This paper studies the subspace clustering problem. Given some data points approximately drawn from a union of subspaces, the goal is to group these data points into their underlying subspaces. Many subspace clustering methods have been proposed and among which sparse subspace clustering and low-rank representation are two representative ones. Despite the different motivations, we observe that many existing methods own the common block diagonal property, which possibly leads to correct clustering, yet with their proofs given case by case. In this work, we consider a general formulation and provide a unified theoretical guarantee of the block diagonal property. The block diagonal property of many existing methods falls into our special case. Second, we observe that many existing methods approximate the block diagonal representation matrix by using different structure priors, e.g., sparsity and low-rankness, which are indirect. We propose the first block diagonal matrix induced regularizer for directly pursuing the block diagonal matrix. With this regularizer, we solve the subspace clustering problem by Block Diagonal Representation (BDR), which uses the block diagonal structure prior. The BDR model is nonconvex and we propose an alternating minimization solver and prove its convergence. Experiments on real datasets demonstrate the effectiveness of BDR.

Includes members:
  Member "oracle based active set algorithm for scalable elastic net subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "a unified alternating direction method of multipliers by majorization minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "correntropy induced l2 graph for robust subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "three stage subspace clustering framework with graph based transformation and optimization")
  Member "correlation adaptive subspace segmentation by trace lasso":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self supervised convolutional subspace clustering network")
  Member "scalable sparse subspace clustering by orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "guess who rated this movie identifying users through subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "robust and efficient subspace segmentation via least squares regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning mixtures of linear regressions in subexponential time via fourier moments")

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)