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: "approximating k ell center clustering for curves"
  Abstract: The Euclidean $k$-center problem is a classical problem that has been extensively studied in many areas of computer science. Given a set $\mathcal{G}$ of $n$ points in Euclidean space, the problem is to determine a set $\mathcal{C}$ of $k$ centers (not necessarily part of $\mathcal{G}$) such that the maximum distance between a point in $\mathcal{G}$ and its nearest neighbor in $\mathcal{C}$ is minimized. In this paper we study the corresponding $(k,\ell)$-center problem for polygonal curves under the Fr\'echet distance, that is, given a set $\mathcal{G}$ of $n$ polygonal curves in $\mathbb{R}^d$, each of complexity $m$, determine a set $\mathcal{C}$ of $k$ polygonal curves in $\mathbb{R}^d$, each of complexity $\ell$, such that the maximum Fr\'echet distance of a curve in $\mathcal{G}$ to its closest curve in $\mathcal{C}$ is minimized. #R##N#We show that there is no polynomial-time $(\frac{3}{2}-\varepsilon)$-approximation algorithm for any $\varepsilon~>~0$ unless $\mathsf{P}=\mathsf{NP}$. This bound even holds for one-dimensional curves under the continuous Fr\'echet distance, and is further improved to $(3\sin(\frac{\pi}{3})-\varepsilon)$ if the curves may lie in the plane and if the discrete Fr\'echet distance is used. These hardness results hold even when $k=1$, i.e., for the minimum-enclosing ball version of the problem. At the same time, we prove that a careful adaptation of Gonzalez' algorithm in combination with a curve simplification yields a $3$-approximation in any dimension, provided that an optimal simplification can be computed exactly. We give a comprehensive analysis of this approximation scheme in spaces of arbitrary dimension and for both the discrete and continuous Fr\'echet distances. Overall, our results significantly extend and improve the known approximation bounds for the $(k,\ell)$-center clustering problem.

Connected through hyperedge HE-1 (induced by source paper "walking the dog fast in practice algorithm engineering of the fr echet distance"):
  with members:
    - "fresh fr echet similarity with hashing"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "four soviets walk the dog improved bounds for computing the frechet distance"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "intermittent map matching with the discrete frechet distance"

Connected through hyperedge HE-2 (induced by source paper "coresets for k l clustering under the frechet distance"):
  with members:
    - "greedy bi criteria approximations for k medians and k means"
    - "local search yields a ptas for k means in doubling metrics"
    - "technical report of participation in higgs boson machine learning challenge"

Connected through hyperedge HE-3 (induced by source paper "on computing a center persistence diagram"):
  with members:
    - "approximate nearest neighbors in the space of persistence diagrams"
    - "geometry helps to compare persistence diagrams"
    - "on the closest string and substring problems"

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.CG(Computational Geometry)