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 "schatten norms in matrix streams hello sparsity goodbye dimension"):
  Abstract: The spectrum of a matrix contains important structural information about the underlying data, and hence there is considerable interest in computing various functions of the matrix spectrum. A fundamental example for such functions is the $l_p$-norm of the spectrum, called the Schatten $p$-norm of the matrix. Large matrices representing real-world data are often \emph{sparse} (most entries are zeros) or \emph{doubly sparse}, i.e., sparse in both rows and columns. These large matrices are usually accessed as a \emph{stream} of updates, typically organized in \emph{row-order}. In this setting, where space (memory) is the limiting resource, computing spectral functions is an expensive task and known algorithms require space that is polynomial in the dimension of the matrix, even for sparse matrices. Thus, it is highly desirable to design algorithms requiring significantly smaller space. #R##N#We answer this challenge by providing the first algorithm that uses space \emph{independent of the matrix dimension} to compute the Schatten $p$-norm of a doubly-sparse matrix presented in row order. Instead, our algorithm uses space polynomial in the sparsity parameter $k$ and makes $O(p)$ passes over the data stream. We further prove that multiple passes are unavoidable in this setting and show several extensions of our primary technique, including stronger upper bounds for special matrix families, algorithms for the more difficult turnstile model, and a trade-off between space requirements and number of passes.

Includes members:
  Member "spectrum approximation beyond fast matrix multiplication algorithms and hardness":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions")
  Member "approximating the spectrum of a graph":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "just slaq when you approximate accurate spectral distances for web scale graphs")
  Member "embeddings of schatten norms with applications to data streams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "querying a matrix through matrix vector products")
  Member "weighted schatten p norm minimization for image denoising and background subtraction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed estimation of the inverse hessian by determinantal averaging")
  Member "spectrum estimation from samples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the dimensionality of word embedding")
  Member "on approximating functions of the singular values in a stream":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "querying a matrix through matrix vector products")
  Member "matrix norms in data streams faster multi pass and row order":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "querying a matrix through matrix vector products")

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)