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 "dimension independent matrix square using mapreduce"):
  Abstract: We compute the singular values of an $m \times n$ sparse matrix $A$ in a distributed setting, without communication dependence on $m$, which is useful for very large $m$. In particular, we give a simple nonadaptive sampling scheme where the singular values of $A$ are estimated within relative error with constant probability. Our proven bounds focus on the MapReduce framework, which has become the de facto tool for handling such large matrices that cannot be stored or even streamed through a single machine. #R##N#On the way, we give a general method to compute $A^TA$. We preserve singular values of $A^TA$ with $\epsilon$ relative error with shuffle size $O(n^2/\epsilon^2)$ and reduce-key complexity $O(n/\epsilon^2)$. We further show that if only specific entries of $A^TA$ are required and $A$ has nonnegative entries, then we can reduce the shuffle size to $O(n \log(n) / s)$ and reduce-key complexity to $O(\log(n)/s)$, where $s$ is the minimum cosine similarity for the entries being estimated. All of our bounds are independent of $m$, the larger dimension. We provide open-source implementations in Spark and Scalding, along with experiments in an industrial setting.

Includes members:
  Member "direct qr factorizations for tall and skinny matrices in mapreduce architectures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressed nonnegative matrix factorization is fast and accurate")
  Member "low rank approximation and regression in input sparsity time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "complexity measures for map reduce and comparison to parallel computing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on geographically distributed big data processing using mapreduce")
  Member "dimension independent similarity computation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when hashes met wedges a distributed algorithm for finding high similarity vectors")
  Member "fast approximation of matrix coherence and statistical leverage":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "faster least squares approximation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "randomized spectral clustering in large scale stochastic block models")

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)