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 "efficient structured matrix recovery and nearly linear time algorithms for solving inverse symmetric m matrices"):
  Abstract: In this paper we show how to recover a spectral approximations to broad classes of structured matrices using only a polylogarithmic number of adaptive linear measurements to either the matrix or its inverse. Leveraging this result we obtain faster algorithms for variety of linear algebraic problems. Key results include: #R##N#$\bullet$ A nearly linear time algorithm for solving the inverse of symmetric $M$-matrices, a strict superset of Laplacians and SDD matrices. #R##N#$\bullet$ An $\tilde{O}(n^2)$ time algorithm for solving $n \times n$ linear systems that are constant spectral approximations of Laplacians or more generally, SDD matrices. #R##N#$\bullet$ An $\tilde{O}(n^2)$ algorithm to recover a spectral approximation of a $n$-vertex graph using only $\tilde{O}(1)$ matrix-vector multiplies with its Laplacian matrix. #R##N#The previous best results for each problem either used a trivial number of queries to exactly recover the matrix or a trivial $O(n^\omega)$ running time, where $\omega$ is the matrix multiplication constant. #R##N#We achieve these results by generalizing recent semidefinite programming based linear sized sparsifier results of Lee and Sun (2017) and providing iterative methods inspired by the semistreaming sparsification results of Kapralov, Lee, Musco, Musco and Sidford (2014) and input sparsity time linear system solving results of Li, Miller, and Peng (2013). We hope that by initiating study of these natural problems, expanding the robustness and scope of recent nearly linear time linear system solving research, and providing general matrix recovery machinery this work may serve as a stepping stone for faster algorithms.

Includes members:
  Member "non convex matrix completion against a semi random adversary":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "positive semidefinite programming mixed parallel and width independent")
  Member "an almost linear time algorithm for uniform random spanning tree generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "matrix scaling and balancing via box constrained newton s method and interior point methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "flows in almost linear time via adaptive preconditioning")
  Member "sampling random spanning trees faster than matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications")
  Member "faster algorithms for computing the stationary distribution simulating random walks and more":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "spectral sparsification and regret minimization beyond matrix multiplicative updates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a spectral approach to network design")
  Member "on sketching quadratic forms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "algorithms for lipschitz learning on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "flows in almost linear time via adaptive preconditioning")

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)