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 "geometric median in nearly linear time"):
  Abstract: In this paper we provide faster algorithms for solving the geometric median problem: given $n$ points in $\mathbb{R}^{d}$ compute a point that minimizes the sum of Euclidean distances to the points. This is one of the oldest non-trivial problems in computational geometry yet despite an abundance of research the previous fastest algorithms for computing a $(1+\epsilon)$-approximate geometric median were $O(d\cdot n^{4/3}\epsilon^{-8/3})$ by Chin et. al, $\tilde{O}(d\exp{\epsilon^{-4}\log\epsilon^{-1}})$ by Badoiu et. al, $O(nd+\mathrm{poly}(d,\epsilon^{-1})$ by Feldman and Langberg, and $O((nd)^{O(1)}\log\frac{1}{\epsilon})$ by Parrilo and Sturmfels and Xue and Ye. #R##N#In this paper we show how to compute a $(1+\epsilon)$-approximate geometric median in time $O(nd\log^{3}\frac{1}{\epsilon})$ and $O(d\epsilon^{-2})$. While our $O(d\epsilon^{-2})$ is a fairly straightforward application of stochastic subgradient descent, our $O(nd\log^{3}\frac{1}{\epsilon})$ time algorithm is a novel long step interior point method. To achieve this running time we start with a simple $O((nd)^{O(1)}\log\frac{1}{\epsilon})$ time interior point method and show how to improve it, ultimately building an algorithm that is quite non-standard from the perspective of interior point literature. Our result is one of very few cases we are aware of outperforming traditional interior point theory and the only we are aware of using interior point methods to obtain a nearly linear time algorithm for a canonical optimization problem that traditionally requires superlinear time. We hope our work leads to further improvements in this line of research.

Includes members:
  Member "navigating central path with electrical flows from flows to matchings and back":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster divergence maximization for faster maximum flow")
  Member "runtime guarantees for regression problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster divergence maximization for faster maximum flow")
  Member "a unified framework for approximating and clustering data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating spectral clustering via sampling a review")

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.DS(Data Structures and Algorithms)