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 "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.

Includes members:
  Member "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs":
    also connected through other hyperedges:
      (none)
  Member "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "heterogeneous temporal graph convolutional networks make the community detection much better")
  Member "performance of johnson lindenstrauss transform for k means and k medians clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fair clustering with multiple colors")
  Member "graph reduction by local variation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications")
  Member "scalable spectral clustering using random binning features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable global alignment graph kernel using random features from node embedding to graph embedding")
  Member "graph reduction with spectral and cut guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "copt coordinated optimal transport on graphs")
  Member "spectrally approximating large graphs with smaller graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "copt coordinated optimal transport on graphs")
  Member "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximate similarity search under edit distance using locality sensitive hashing")

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.LG(Machine Learning)