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 "using optimization to find maximum inscribed balls and minimum enclosing balls"):
  Abstract: We study two fundamental problems in computational geometry: finding the maximum inscribed ball (MaxIB) inside a polytope defined by $m$ hyperplanes in a $d$-dimensional space, and finding the minimum enclosing ball (MinEB) of a set of $n$ points in a $d$-dimensional space. We translate both geometry problems into purely algebraic optimization questions, and apply ideas from first-order convex optimizations to obtain simple and nearly-linear-time algorithms. #R##N#For MaxIB, our algorithm produces a $(1-\varepsilon)$-approximate solution in time $\tilde{O}(m d \alpha / \varepsilon)$, compared to the best known running time $\tilde{O}(m d \alpha^3 / \varepsilon^3)$ of Xie, Snoeyink, and Xu [XSX06], where $\alpha \geq 1$ is the aspect ratio of the polytope. For MinEB, our algorithm produces a $(1+\varepsilon)$-approximate solution in time $\tilde{O}(n d / \sqrt{\varepsilon})$. This result matches the previous best running time, while yielding a simpler algorithm. For both problems, we provide an empirical evaluation showing the improved performance of our algorithms.

Includes members:
  Member "using optimization to obtain a width independent parallel simpler and faster positive sdp solver":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "positive semidefinite programming mixed parallel and width independent")
  Member "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improved convergence for ell_ infty and ell_1 regression via iteratively reweighted least squares")
  Member "sublinear optimization for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on coresets for support vector machines")
  Member "new approximation algorithms for minimum enclosing convex shapes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "minimum enclosing ball revisited stability sub linear time algorithms and extension")

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.CG(Computational Geometry)