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: "active orthogonal matching pursuit for sparse subspace clustering"
  Abstract: Sparse subspace clustering (SSC) is a state-of-the-art method for clustering high-dimensional data points lying in a union of low-dimensional subspaces. However, while l#N# 1#N# optimization-based SSC algorithms suffer from high computational complexity, other variants of SSC, such as orthogonal-matching-pursuit-based SSC (OMP-SSC), lose clustering accuracy in pursuit of improving time efficiency. In this letter, we propose a novel active OMP-SSC, which improves clustering accuracy of OMP-SSC by adaptively updating data points and randomly dropping data points in the OMP process, while still enjoying the low computational complexity of greedy pursuit algorithms. We provide heuristic analysis of our approach and explain how these two active steps achieve a better tradeoff between connectivity and separation. Numerical results on both synthetic data and real-world data validate our analyses and show the advantages of the proposed active algorithm.

Connected through hyperedge HE-1 (induced by source paper "restricted connection orthogonal matching pursuit for sparse subspace clustering"):
  with members:
    - "robust subspace clustering by cauchy loss function"
    - "noisy subspace clustering via matching pursuits"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "algebraic clustering of affine subspaces"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-2 (induced by source paper "theory of spectral method for union of subspaces based random geometry graph"):
  with members:
    - "restricted isometry property of gaussian random projection for finite set of subspaces"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "greedy feature selection for subspace clustering"
    - "generalized principal component analysis gpca"
    - "a geometric analysis of subspace clustering with outliers"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "transformed subspace clustering"):
  with members:
    - "kernel transform learning"
    - "deep clustering for unsupervised learning of visual features"
    - "noisy subspace clustering via matching pursuits"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "a deep matrix factorization method for learning attribute representations"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-4 (induced by source paper "evolutionary self expressive models for subspace clustering"):
  with members:
    - "evolutionary clustering via message passing"
    - "theoretical analysis of sparse subspace clustering with missing entries"
    - "noisy subspace clustering via matching pursuits"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "high dimensional low rank plus sparse matrix decomposition"
    - "innovation pursuit a new approach to subspace clustering"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"
    - "accelerated sparse subspace clustering"

Connected through hyperedge HE-5 (induced by source paper "scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs"):
  with members:
    - "efficient sparse subspace clustering by nearest neighbour filtering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "provable deterministic leverage score sampling"
    - "robust and efficient subspace segmentation via least squares regression"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"

Connected through hyperedge HE-6 (induced by source paper "efficient solvers for sparse subspace clustering"):
  with members:
    - "approximating spectral clustering via sampling a review"
    - "large scale sparse subspace clustering using landmarks"
    - "theoretical analysis of sparse subspace clustering with missing entries"
    - "scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs"
    - "unconstrained and curvature constrained shortest path distances and their approximation"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "adaptive admm with spectral penalty parameter selection"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

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.LG(Machine Learning)