human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "filtrated algebraic subspace clustering"
  Abstract: Subspace clustering is the problem of clustering data that lie close to a union of linear subspaces. In the abstract form of the problem, where no noise or other corruptions are present, the data are assumed to lie in general position inside the algebraic variety of a union of subspaces, and the objective is to decompose the variety into its constituent subspaces. Prior algebraic-geometric approaches to this problem require the subspaces to be of equal dimension, or the number of subspaces to be known. Subspaces of arbitrary dimensions can still be recovered in closed form, in terms of all homogeneous polynomials of degree $m$ that vanish on their union, when an upper bound m on the number of the subspaces is given. In this paper, we propose an alternative, provably correct, algorithm for addressing a union of at most $m$ arbitrary-dimensional subspaces, based on the idea of descending filtrations of subspace arrangements. Our algorithm uses the gradient of a vanishing polynomial at a point in the variety to find a hyperplane containing the subspace S passing through that point. By intersecting the variety with this hyperplane, we obtain a subvariety that contains S, and recursively applying the procedure until no non-trivial vanishing polynomial exists, our algorithm eventually identifies S. By repeating this procedure for other points, our algorithm eventually identifies all the subspaces by returning a basis for their orthogonal complement. Finally, we develop a variant of the abstract algorithm, suitable for computations with noisy data. We show by experiments on synthetic and real data that the proposed algorithm outperforms state-of-the-art methods on several occasions, thus demonstrating the merit of the idea of filtrations.

Connected through hyperedge HE-1 (induced by source paper "an algebraic geometric approach for linear regression without correspondences"):
  with members:
    - "permutations unlabeled beyond sampling unknown"
    - "homomorphic sensing"
    - "an overview of robust subspace recovery"
    - "signal recovery from unlabeled samples"
    - "linear regression without correspondence"
    - "unlabeled sensing with random linear measurements"
    - "dual principal component pursuit"
    - "algebraic clustering of affine subspaces"

Connected through hyperedge HE-2 (induced by source paper "theoretical analysis of sparse subspace clustering with missing entries"):
  with members:
    - "hyperplane clustering via dual principal component pursuit"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "on deterministic conditions for subspace clustering under missing data"
    - "robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"

Connected through hyperedge HE-3 (induced by source paper "an algebraic geometric approach to shuffled linear regression"):
  with members:
    - "homomorphic sensing"
    - "an overview of robust subspace recovery"
    - "signal recovery from unlabeled samples"
    - "linear regression without correspondence"
    - "unlabeled sensing with random linear measurements"
    - "timing channels with multiple identical quanta"
    - "blind compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "cur decompositions similarity matrices and subspace clustering"):
  with members:
    - "joint active learning with feature selection via cur matrix decomposition"
    - "filtrated spectral algebraic subspace clustering"
    - "optimal cur matrix decompositions"
    - "cur algorithm for partially observed matrices"
    - "classifying and visualizing motion capture sequences using deep neural networks"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "hybrid linear modeling via local best fit flats"

Connected through hyperedge HE-5 (induced by source paper "hyperplane clustering via dual principal component pursuit"):
  with members:
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "dual principal component pursuit"
    - "robust computation of linear models by convex relaxation"
    - "filtrated spectral algebraic subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "algebraic clustering of affine subspaces"):
  with members:
    - "sparse subspace clustering by orthogonal matching pursuit"
    - "filtrated spectral algebraic subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"

Question: Please tell me which class the center node 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)