human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
  Abstract: We show how to solve directed Laplacian systems in nearly-linear time. Given a linear system in an $n \times n$ Eulerian directed Laplacian with $m$ nonzero entries, we show how to compute an $\epsilon$-approximate solution in time $O(m \log^{O(1)} (n) \log (1/\epsilon))$. Through reductions from [Cohen et al. FOCS'16] , this gives the first nearly-linear time algorithms for computing $\epsilon$-approximate solutions to row or column diagonally dominant linear systems (including arbitrary directed Laplacians) and computing $\epsilon$-approximations to various properties of random walks on directed graphs, including stationary distributions, personalized PageRank vectors, hitting times, and escape probabilities. These bounds improve upon the recent almost-linear algorithms of [Cohen et al. STOC'17], which gave an algorithm to solve Eulerian Laplacian systems in time $O((m+n2^{O(\sqrt{\log n \log \log n})})\log^{O(1)}(n \epsilon^{-1}))$. #R##N#To achieve our results, we provide a structural result that we believe is of independent interest. We show that Laplacians of all strongly connected directed graphs have sparse approximate LU-factorizations. That is, for every such directed Laplacian $ {\mathbf{L}}$, there is a lower triangular matrix $\boldsymbol{\mathit{\mathfrak{L}}}$ and an upper triangular matrix $\boldsymbol{\mathit{\mathfrak{U}}}$, each with at most $\tilde{O}(n)$ nonzero entries, such that their product $\boldsymbol{\mathit{\mathfrak{L}}} \boldsymbol{\mathit{\mathfrak{U}}}$ spectrally approximates $ {\mathbf{L}}$ in an appropriate norm. This claim can be viewed as an analogue of recent work on sparse Cholesky factorizations of Laplacians of undirected graphs. We show how to construct such factorizations in nearly-linear time and prove that, once constructed, they yield nearly-linear time algorithms for solving directed Laplacian systems.

Connected through hyperedge HE-1 (induced by source paper "spectral sparsification via bounded independence sampling"):
  with members:
    - "faster energy maximization for faster maximum flow"
    - "deterministic approximation of random walks in small space"
    - "high precision estimation of random walks in small space"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"

Connected through hyperedge HE-2 (induced by source paper "flows in almost linear time via adaptive preconditioning"):
  with members:
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "expander decomposition and pruning faster stronger and simpler"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "much faster algorithms for matrix scaling"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "asymptotic behavior of ell_p based laplacian regularization in semi supervised learning"
    - "computing maximum flow with augmenting electrical flows"

Connected through hyperedge HE-3 (induced by source paper "high precision estimation of random walks in small space"):
  with members:
    - "deterministic approximation of random walks in small space"
    - "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "spectral sparsification of random walk matrix polynomials"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"

Connected through hyperedge HE-4 (induced by source paper "electrical flows over spanning trees"):
  with members:
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "sampling random spanning trees faster than matrix multiplication"
    - "computing maximum flow with augmenting electrical flows"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"
    - "graph sparsification by effective resistances"
    - "nearly tight low stretch spanning trees"

Connected through hyperedge HE-5 (induced by source paper "solving linear programs in the current matrix multiplication time"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "incomplete nested dissection"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "computing maximum flow with augmenting electrical flows"
    - "geometric median in nearly linear time"
    - "approximate gaussian elimination for laplacians fast sparse and simple"

Connected through hyperedge HE-6 (induced by source paper "towards scalable spectral sparsification of directed graphs"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "similarity aware spectral sparsification by edge filtering"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "twice ramanujan sparsifiers"
    - "spectral sparsification of graphs"
    - "graph sparsification by effective resistances"

Question: Please tell me which class the center node 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)