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 "coresets for k l clustering under the frechet distance"):
  Abstract: Clustering is the task of partitioning a given set of geometric objects. This is thoroughly studied when the objects are points in the euclidean space. There are also several approaches for points in general metric spaces. In this thesis we consider clustering polygonal curves, i.e., curves composed of line segments, under the Fr\'echet distance. We obtain clusterings by minimizing an objective function, which yields a set of centers that induces a partition of the input. #R##N#The objective functions we consider is the so called $(k,l)$-\textsc{center}, where we are to find the $k$ center-curves that minimize the maximum distance between any input-curve and a nearest center-curve and the so called $k$-\textsc{median}, where we are to find the $k$ center-curves that minimize the sum of the distances between the input-curves and a nearest center-curve. #R##N#Given a set of $n$ polygonal curves, we are interested in reducing this set to an $\epsilon$-coreset, i.e., a notably smaller set of curves that has a very similar clustering-behavior. We develop a construction method for such $\epsilon$-coresets for the $(k,l)$-\textsc{center}, that yields $\epsilon$-coresets of size of a polynomial of $\frac{1}{\epsilon}$, in time linear in $n$ and a polynomial of $\frac{1}{\epsilon}$, for line segments. Also, we develop a construction technique for the $(k,l)$-\textsc{center} that yields $\epsilon$-coresets of size exponential in $m$ with basis $\frac{1}{\epsilon}$, in time sub-quadratic in $n$ and exponential in $m$ with basis $\frac{1}{\epsilon}$, for general polygonal curves. Finally, we develop a construction method for the $k$-\textsc{median}, that yields $\epsilon$-coresets of size polylogarithmic in $n$ and a polynomial of $\frac{1}{\epsilon}$, in time linear in $n$ and a polynomial of $\frac{1}{\epsilon}$.

Includes members:
  Member "approximating k ell center clustering for curves":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "walking the dog fast in practice algorithm engineering of the fr echet distance")
  Member "greedy bi criteria approximations for k medians and k means":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "low rank approximation from communication complexity")
  Member "local search yields a ptas for k means in doubling metrics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "k means few more steps yield constant approximation")
  Member "technical report of participation in higgs boson machine learning challenge":
    also connected through other hyperedges:
      (none)

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)