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 systems of linear equations locally and asynchronously"
  Abstract: We consider approximating a single component of the solution to a system of linear equations $Ax = b$, where $A$ is an invertible real matrix and $b \in \mathbb{R}^n$. If $A$ is either diagonally dominant or positive definite, we can equivalently solve for $x_i$ in $x = Gx + z$ for some $G$ and $z$ such that spectral radius $\rho(G) < 1$. Existing algorithms either focus on computing the full vector $x$ or use Monte Carlo methods to estimate a component $x_i$ under the condition $\|G\|_{\infty} < 1$. We consider the setting where $n$ is large, yet $G$ is sparse, i.e., each row has at most $d$ nonzero entries. We present synchronous and asynchronous randomized variants of a local algorithm which relies on the Neumann series characterization of the component $x_i$, and allows us to limit the sparsity of the vectors involved in the computation, leading to improved convergence rates. Both variants of our algorithm produce an estimate $\hat{x}_i$ such that $|\hat{x}_i - x_i| \leq \epsilon \|x\|_2$, and we provide convergence guarantees when $\|G\|_2 < 1$. We prove that the synchronous local algorithm uses at most $O(\min(d \epsilon^{\ln(d)/\ln(\|G\|_2)}, dn\ln(\epsilon)/\ln(\|G\|_2)))$ multiplications. The asynchronous local algorithm adaptively samples one coordinate to update among the nonzero coordinates of the current iterate in each time step. We prove with high probability that the error contracts by a time varying factor in each step, guaranteeing that the algorithm converges to the correct solution. With probability at least $1 - \delta$, the asynchronous randomized algorithm uses at most $O(\min(d (\epsilon \sqrt{\delta/5})^{-d/(1-\|G\|_2)}, -dn \ln (\epsilon \sqrt{\delta})/(1-\|G\|_2)))$ multiplications. Thus our algorithms obtain an approximation for $x_i$ in constant time with respect to the size of the matrix when $d = O(1)$ and $1/(1-\|G\|_2) = O(1)$ as a function of $n$.

Connected through hyperedge HE-1 (induced by source paper "on the role of clustering in personalized pagerank estimation"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "asynchronous approximation of a single component of the solution to a linear system"
    - "pagerank beyond the web"
    - "computing the stationary distribution locally"
    - "personalized pagerank to a target node"
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-2 (induced by source paper "on approximating the stationary distribution of time reversible markov chains"):
  with members:
    - "fast bidirectional probability estimation in markov models"
    - "bidirectional pagerank estimation from average case to worst case"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "sublinear algorithms for local graph centrality estimation"
    - "computing the stationary distribution locally"

Connected through hyperedge HE-3 (induced by source paper "network flows that solve linear equations"):
  with members:
    - "decentralized gradient algorithm for solution of a linear equation"
    - "a distributed algorithm for solving a linear algebraic equation"
    - "a fast distributed solver for symmetric diagonally dominant linear equations"
    - "distributed strongly convex optimization"
    - "fast distributed gradient methods"
    - "cooperative convex optimization in networked systems augmented lagrangian algorithms with directed gossip communication"
    - "randomized optimal consensus of multi agent systems"
    - "reaching an optimal consensus dynamical systems that compute intersections of convex sets"

Connected through hyperedge HE-4 (induced by source paper "a fast distributed solver for symmetric diagonally dominant linear equations"):
  with members:
    - "near linear work parallel sdd solvers low diameter decomposition and low stretch subgraphs"
    - "approaching optimality for solving sdd systems"
    - "faster generation of random spanning trees"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"
    - "solving elliptic finite element systems in near linear time with support preconditioners"

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)