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: "approximating spectral clustering via sampling a review"
  Abstract: Spectral clustering refers to a family of well-known unsupervised learning algorithms. Rather than attempting to cluster points in their native domain, one constructs a (usually sparse) similarity graph and computes the principal eigenvectors of its Laplacian. The eigenvectors are then interpreted as transformed points and fed into a k-means clustering algorithm. As a result of this non-linear transformation, it becomes possible to use a simple centroid-based algorithm in order to identify non-convex clusters, something that was otherwise impossible. Unfortunately, what makes spectral clustering so successful is also its Achilles heel: forming a graph and computing its dominant eigenvectors can be computationally prohibitive when dealing with more than a few tens of thousands of points. In this chapter, we review the principal research efforts aiming to reduce this computational cost. We focus on methods that come with a theoretical control on the clustering performance and incorporate some form of sampling in their operation. Such methods abound in the machine learning, numerical linear algebra, and graph signal processing literature and, among others, include Nystrom-approximation, landmarks, coarsening, coresets, and compressive spectral clustering. We present the approximation guarantees available for each and discuss practical merits and limitations. Surprisingly, despite the breadth of the literature explored, we conclude that there is still a gap between theory and practice: the most scalable methods are only intuitively motivated or loosely controlled, whereas those that come with end-to-end guarantees rely on strong assumptions or enable a limited gain of computation time.

Connected through hyperedge HE-1 (induced by source paper "efficient solvers for sparse subspace clustering"):
  with members:
    - "large scale sparse subspace clustering using landmarks"
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "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)