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 "towards more efficient spsd matrix approximation and cur matrix decomposition"):
  Abstract: Symmetric positive semi-definite (SPSD) matrix approximation methods have been extensively used to speed up large-scale eigenvalue computation and kernel learning methods. The standard sketch based method, which we call the prototype model, produces relatively accurate approximations, but is inefficient on large square matrices. The Nystr\"om method is highly efficient, but can only achieve low accuracy. In this paper we propose a novel model that we call the {\it fast SPSD matrix approximation model}. The fast model is nearly as efficient as the Nystr\"om method and as accurate as the prototype model. We show that the fast model can potentially solve eigenvalue problems and kernel learning problems in linear time with respect to the matrix size $n$ to achieve $1+\epsilon$ relative-error, whereas both the prototype model and the Nystr\"om method cost at least quadratic time to attain comparable error bound. Empirical comparisons among the prototype model, the Nystr\"om method, and our fast model demonstrate the superiority of the fast model. We also contribute new understandings of the Nystr\"om method. The Nystr\"om method is a special instance of our fast model and is approximation to the prototype model. Our technique can be straightforwardly applied to make the CUR matrix decomposition more efficiently computed without much affecting the accuracy.

Includes members:
  Member "optimal approximate matrix product in terms of stable rank":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "speeding up linear programming using randomized linear algebra")
  Member "sketching as a tool for numerical linear algebra":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "scalable kernel methods via doubly stochastic gradients":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural kernels without tangents")
  Member "optimal cur matrix decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "spsd matrix approximation vis column selection theories algorithms and extensions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast generalized matrix regression with applications in machine learning")
  Member "revisiting the nystrom method for improved large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "low rank approximation and regression in input sparsity time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "multi scale link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey of link recommendation for social networks methods theoretical foundations and future research directions")

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)