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 "incremental eigenpair computation for graph laplacian matrices theory and applications"):
  Abstract: The smallest eigenvalues and the associated eigenvectors (i.e., eigenpairs) of a graph Laplacian matrix have been widely used in spectral clustering and community detection. However, in real-life applications the number of clusters or communities (say, $K$) is generally unknown a-priori. Consequently, the majority of the existing methods either choose $K$ heuristically or they repeat the clustering method with different choices of $K$ and accept the best clustering result. The first option, more often, yields suboptimal result, while the second option is computationally expensive. In this work, we propose an incremental method for constructing the eigenspectrum of the graph Laplacian matrix. This method leverages the eigenstructure of graph Laplacian matrix to obtain the $K$-th smallest eigenpair of the Laplacian matrix given a collection of all previously computed $K-1$ smallest eigenpairs. Our proposed method adapts the Laplacian matrix such that the batch eigenvalue decomposition problem transforms into an efficient sequential leading eigenpair computation problem. As a practical application, we consider user-guided spectral clustering. Specifically, we demonstrate that users can utilize the proposed incremental method for effective eigenpair computation and for determining the desired number of clusters based on multiple clustering metrics.

Includes members:
  Member "revisiting random binning features fast convergence and strong parallelizability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "doctor of crosswise reducing over parametrization in neural networks")
  Member "principled multilayer network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aligraph a comprehensive graph neural network platform")
  Member "name disambiguation in anonymized graphs using network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fondue a framework for node disambiguation using network embeddings")
  Member "phase transitions and a model order selection criterion for spectral graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable spectral clustering using random binning features")
  Member "nous construction and querying of dynamic knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "primme_svds a high performance preconditioned svd solver for accurate large scale computations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scalable global alignment graph kernel using random features from node embedding to graph embedding")
  Member "trust from the past bayesian personalized ranking based link prediction in knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep collaborative filtering with multi aspect information in heterogeneous networks")

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)