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: "faster algorithms for computing the stationary distribution simulating random walks and more"
  Abstract: In this paper, we provide faster algorithms for computing various fundamental quantities associated with random walks on a directed graph, including the stationary distribution, personalized PageRank vectors, hitting times, and escape probabilities. In particular, on a directed graph with $n$ vertices and $m$ edges, we show how to compute each quantity in time $\tilde{O}(m^{3/4}n+mn^{2/3})$, where the $\tilde{O}$ notation suppresses polylogarithmic factors in $n$, the desired accuracy, and the appropriate condition number (i.e. the mixing time or restart probability). #R##N#Our result improves upon the previous fastest running times for these problems; previous results either invoke a general purpose linear system solver on a $n\times n$ matrix with $m$ non-zero entries, or depend polynomially on the desired error or natural condition number associated with the problem (i.e. the mixing time or restart probability). For sparse graphs, we obtain a running time of $\tilde{O}(n^{7/4})$, breaking the $O(n^{2})$ barrier of the best running time one could hope to achieve using fast matrix multiplication. #R##N#We achieve our result by providing a similar running time improvement for solving directed Laplacian systems, a natural directed or asymmetric analog of the well studied symmetric or undirected Laplacian systems. We show how to solve such systems in time $\tilde{O}(m^{3/4}n+mn^{2/3})$, and efficiently reduce a broad range of problems to solving $\tilde{O}(1)$ directed Laplacian systems on Eulerian graphs. We hope these results and our analysis open the door for further study into directed spectral graph theory.

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"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"
    - "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 "trapping problem on star type graphs with applications"):
  with members:
    - "on the structure and computation of random walk times in finite graphs"
    - "a vertex similarity index for better personalized recommendation"
    - "reliability and survivability analysis of data center network topologies"
    - "convolutional random walk networks for semantic image segmentation"
    - "the constant inapproximability of the parameterized dominating set problem"
    - "planar graphs random walks and bipartiteness testing"
    - "estimating a random walk first passage time from noisy or delayed observations"

Connected through hyperedge HE-3 (induced by source paper "netsmf large scale network embedding as sparse matrix factorization"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "representation learning on graphs methods and applications"
    - "network efficient distributed word2vec training system for large vocabularies"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "high precision estimation of random walks in small space"):
  with members:
    - "deterministic approximation of random walks in small space"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"
    - "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-5 (induced by source paper "deterministic approximation of random walks in small space"):
  with members:
    - "density independent algorithms for sparsifying k step random walks"
    - "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"
    - "a framework for analyzing resparsification algorithms"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "sparsified cholesky solvers for sdd linear systems"
    - "spectral sparsification of random walk matrix polynomials"
    - "scalable parallel factorizations of sdd matrices and efficient sampling for gaussian graphical models"
    - "an efficient parallel solver for sdd linear systems"

Connected through hyperedge HE-6 (induced by source paper "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "fully dynamic effective resistances"
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "nearly tight bounds for sandpile transience on the grid"

Connected through hyperedge HE-7 (induced by source paper "solving directed laplacian systems in nearly linear time through sparse lu factorizations"):
  with members:
    - "sampling random spanning trees faster than matrix multiplication"
    - "computing maximum flow with augmenting electrical flows"
    - "on fully dynamic graph sparsifiers"
    - "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"
    - "sparsified cholesky solvers for sdd linear systems"
    - "fast generation of random spanning trees and the effective resistance metric"

Connected through hyperedge HE-8 (induced by source paper "efficient structured matrix recovery and nearly linear time algorithms for solving inverse symmetric m matrices"):
  with members:
    - "non convex matrix completion against a semi random adversary"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "sampling random spanning trees faster than matrix multiplication"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "on sketching quadratic forms"
    - "algorithms for lipschitz learning on graphs"
    - "nearly linear time approximation schemes for mixed packing covering and facility location linear programs"

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)