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 "distributed kernel principal component analysis"):
  Abstract: Kernel Principal Component Analysis (KPCA) is a key technique in machine learning for extracting the nonlinear structure of data and pre-processing it for downstream learning algorithms. We study the distributed setting in which there are multiple workers, each holding a set of points, who wish to compute the principal components of the union of their pointsets. Our main result is a communication efficient algorithm that takes as input arbitrary data points and computes a set of global principal components, that give relative-error approximation for polynomial kernels, or give relative-error approximation with an arbitrarily small additive error for a wide family of kernels including Gaussian kernels. #R##N#While recent work shows how to do PCA in a distributed setting, the kernel setting is significantly more challenging. Although the "kernel trick" is useful for efficient computation, it is unclear how to use it to reduce communication. The main problem with previous work is that it achieves communication proportional to the dimension of the data points, which would be proportional to the dimension of the feature space, or to the number of examples, both of which could be very large. We instead first select a small subset of points whose span contains a good approximation (the column subset selection problem, CSS), and then use sketching for low rank approximation to achieve our result. The column subset selection is done using a careful combination of oblivious subspace embeddings for kernels, oblivious leverage score approximation, and adaptive sampling. These lead to nearly optimal communication bound for CSS, and also lead to nearly optimal communication for KPCA in the constant approximation region. Experiments on large scale real world datasets show the efficacy of our algorithm.

Includes members:
  Member "communication optimal distributed principal component analysis in the column partition model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effective sketching methods for value function approximation")
  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 "improved distributed principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the communication complexity of optimization")
  Member "principal component analysis and higher correlations for distributed data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the communication complexity of optimization")
  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 "osnap faster numerical linear algebra algorithms via sparser subspace embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sketching transformed matrices with applications to natural language processing")

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)