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 "sketching transformed matrices with applications to natural language processing"):
  Abstract: Suppose we are given a large matrix $A=(a_{i,j})$ that cannot be stored in memory but is in a disk or is presented in a data stream. However, we need to compute a matrix decomposition of the entry-wisely transformed matrix, $f(A):=(f(a_{i,j}))$ for some function $f$. Is it possible to do it in a space efficient way? Many machine learning applications indeed need to deal with such large transformed matrices, for example word embedding method in NLP needs to work with the pointwise mutual information (PMI) matrix, while the entrywise transformation makes it difficult to apply known linear algebraic tools. Existing approaches for this problem either need to store the whole matrix and perform the entry-wise transformation afterwards, which is space consuming or infeasible, or need to redesign the learning method, which is application specific and requires substantial remodeling. #R##N#In this paper, we first propose a space-efficient sketching algorithm for computing the product of a given small matrix with the transformed matrix. It works for a general family of transformations with provable small error bounds and thus can be used as a primitive in downstream learning tasks. We then apply this primitive to a concrete application: low-rank approximation. We show that our approach obtains small error and is efficient in both space and time. We complement our theoretical results with experiments on synthetic and real data.

Includes members:
  Member "solving tall dense linear programs in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster divergence maximization for faster maximum flow")
  Member "tight bounds for the subspace sketch problem with applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "total least squares regression in input sparsity time")
  Member "optimal sketching for kronecker product regression and low rank approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "total least squares regression in input sparsity time")
  Member "a near optimal algorithm for approximating the john ellipsoid":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "total least squares regression in input sparsity time")
  Member "regularized weighted low rank approximation":
    also connected through other hyperedges:
      (none)
  Member "robust and sample optimal algorithms for psd low rank approximation":
    also connected through other hyperedges:
      (none)
  Member "dimensionality reduction for tukey regression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "total least squares regression in input sparsity time")
  Member "total least squares regression in input sparsity time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a near optimal algorithm for approximating the john ellipsoid")

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.DS(Data Structures and Algorithms)