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 "spectrum approximation beyond fast matrix multiplication algorithms and hardness"):
  Abstract: Understanding the singular value spectrum of a matrix $A \in \mathbb{R}^{n \times n}$ is a fundamental task in countless applications. In matrix multiplication time, it is possible to perform a full SVD and directly compute the singular values $\sigma_1,...,\sigma_n$. However, little is known about algorithms that break this runtime barrier. #R##N#Using tools from stochastic trace estimation, polynomial approximation, and fast system solvers, we show how to efficiently isolate different ranges of $A$'s spectrum and approximate the number of singular values in these ranges. We thus effectively compute a histogram of the spectrum, which can stand in for the true singular values in many applications. #R##N#We use this primitive to give the first algorithms for approximating a wide class of symmetric matrix norms in faster than matrix multiplication time. For example, we give a $(1 + \epsilon)$ approximation algorithm for the Schatten-$1$ norm (the nuclear norm) running in just $\tilde O((nnz(A)n^{1/3} + n^2)\epsilon^{-3})$ time for $A$ with uniform row sparsity or $\tilde O(n^{2.18} \epsilon^{-3})$ time for dense matrices. The runtime scales smoothly for general Schatten-$p$ norms, notably becoming $\tilde O (p \cdot nnz(A) \epsilon^{-3})$ for any $p \ge 2$. #R##N#At the same time, we show that the complexity of spectrum approximation is inherently tied to fast matrix multiplication in the small $\epsilon$ regime. We prove that achieving milder $\epsilon$ dependencies in our algorithms would imply faster than matrix multiplication time triangle detection for general graphs. This further implies that highly accurate algorithms running in subcubic time yield subcubic time matrix multiplication. As an application of our bounds, we show that precisely computing all effective resistances in a graph in less than matrix multiplication time is likely difficult, barring a major algorithmic breakthrough.

Includes members:
  Member "fast estimation of approximate matrix ranks using spectral densities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "find the dimension that counts fast dimension estimation and krylov pca")
  Member "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "embeddings of schatten norms with applications to data streams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "improved stochastic trace estimation using mutually unbiased bases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration")
  Member "weighted schatten p norm minimization for image denoising and background subtraction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "sketches for matrix norms faster smaller and more general":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "numerical linear algebra in the sliding window model")
  Member "solving ridge regression using sketched preconditioned svrg":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal sketching bounds for exp concave stochastic minimization")
  Member "faster eigenvector computation via shift and invert preconditioning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "principal component projection and regression in nearly linear time through asymmetric svrg")

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)