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 "high precision estimation of random walks in small space"):
  Abstract: In this paper, we provide a deterministic $\tilde{O}(\log N)$-space algorithm for estimating the random walk probabilities on Eulerian directed graphs (and thus also undirected graphs) to within inverse polynomial additive error ($\epsilon=1/\text{poly}(N)$) where $N$ is the length of the input. Previously, this problem was known to be solvable by a randomized algorithm using space $O(\log N)$ (Aleliunas et al., FOCS `79) and by a deterministic algorithm using space $O(\log^{3/2} N)$ (Saks and Zhou, FOCS `95 and JCSS `99), both of which held for arbitrary directed graphs but had not been improved even for undirected graphs. We also give improvements on the space complexity of both of these previous algorithms for non-Eulerian directed graphs when the error is negligible ($\epsilon=1/N^{\omega(1)}$), generalizing what Hoza and Zuckerman (FOCS `18) recently showed for the special case of distinguishing whether a random walk probability is $0$ or greater than $\epsilon$. #R##N#We achieve these results by giving new reductions between powering Eulerian random-walk matrices and inverting Eulerian Laplacian matrices, providing a new notion of spectral approximation for Eulerian graphs that is preserved under powering, and giving the first deterministic $\tilde{O}(\log N)$-space algorithm for inverting Eulerian Laplacian matrices. The latter algorithm builds on the work of Murtagh et al. (FOCS `17) that gave a deterministic $\tilde{O}(\log N)$-space algorithm for inverting undirected Laplacian matrices, and the work of Cohen et al. (FOCS `19) that gave a randomized $\tilde{O}(N)$-time algorithm for inverting Eulerian Laplacian matrices. A running theme throughout these contributions is an analysis of "cycle-lifted graphs," where we take a graph and "lift" it to a new graph whose adjacency matrix is the tensor product of the original adjacency matrix and a directed cycle (or variants of one).

Includes members:
  Member "deterministic approximation of random walks in small space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "solving directed laplacian systems in nearly linear time through sparse lu factorizations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "faster algorithms for computing the stationary distribution simulating random walks and more":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "approximate gaussian elimination for laplacians fast sparse and simple":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "almost linear time algorithms for markov chains and new spectral primitives for directed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "flows in almost linear time via adaptive preconditioning")
  Member "sparsified cholesky and multigrid solvers for connection laplacians":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "spectral sparsification of random walk matrix polynomials":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")

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.CC(Computational Complexity)