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: "coordinate methods for accelerating ell_ infty regression and faster approximate maximum flow"
  Abstract: In this paper we provide faster algorithms for approximately solving $\ell_{\infty}$ regression, a fundamental problem prevalent in both combinatorial and continuous optimization. In particular we provide an accelerated coordinate descent method which converges in $k$ iterations at a $O\left(\frac{1}{k}\right)$ rate independent of the dimension of the problem, and whose iterations can be implemented cheaply for many structured matrices. Our algorithm can be viewed as an alternative approach to the recent breakthrough result of Sherman which achieves a similar running time improvement over classic algorithmic approaches, i.e. smoothing and gradient descent, which either converge at a $O\left(\frac{1}{\sqrt{k}}\right)$ rate or have running times with a worse dependence on problem parameters. #R##N#We show how to use our algorithm to achieve a runtime of $\tilde{O}\left(m + \frac{\sqrt{ns}}{\epsilon}\right)$ to compute an $\epsilon$-approximate maximum flow, for an undirected graph with $m$ edges, $n$ vertices, and where $s$ is the squared $\ell_2$ norm of the congestion of any optimal flow. As $s = O(m)$ this yields a running time of $\tilde{O}\left(m + \frac{\sqrt{nm}}{\epsilon}\right)$, generically improving upon the previous best known runtime of $\tilde{O}\left(\frac{m}{\epsilon}\right)$ by Sherman whenever the graph is slightly dense. Moreover, we show how to achieve improved exact algorithms for maximum flow on a variety of unit capacity graphs. #R##N#We achieve these results by providing an accelerated coordinate descent method exploiting dynamic measures of coordinate smoothness for smoothed $\ell_{\infty}$ regression. Our analysis leverages the structure of the Hessian of the smoothed problem via a bound on its trace, as well as techniques for exploiting sparsity of the constraint matrix for faster sampling and smoothness estimates.

Connected through hyperedge HE-1 (induced by source paper "faster p norm minimizing flows via smoothed q norm problems"):
  with members:
    - "fast provably convergent irls algorithm for p norm linear regression"
    - "flows in almost linear time via adaptive preconditioning"
    - "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"
    - "fast generation of random spanning trees and the effective resistance metric"
    - "approximate undirected maximum flows in o m polylog n time"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"

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)