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 "on the hardness of computing an average curve"):
  Abstract: We study the complexity of clustering curves under k-median and k-center objectives in the metric space of the Frechet distance and related distance measures. The k-center problem has recently been shown to be NP-hard, even in the case where k=1, i.e. the minimum enclosing ball under the Frechet distance. We extend these results by showing that also the k-median problem is NP-hard for k=1. Furthermore, we show that the 1-median problem is W[1]-hard with the number of curves as parameter. We show this under the discrete and continuous Frechet and Dynamic Time Warping (DTW) distance. Our result generalizes an earlier result by Bulteau et al. from 2018 for a variant of DTW that uses squared distances. Moreover, closing some gaps in the literature, we show positive results for a variant where the center curve may have complexity at most l under the discrete Frechet distance. In particular, for fixed k,l and e, we give (1+e)-approximation algorithms for the (k,l)-median and (k,l)-center objectives and a polynomial-time exact algorithm for the (k,l)-center objective.

Includes members:
  Member "hardness of consensus problems for circular strings and time series averaging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an average compress algorithm for the sample mean problem under dynamic time warping")
  Member "tight hardness results for consensus problems on circular strings and time series":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exact mean computation in dynamic time warping spaces")
  Member "approximating k median via pseudo approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fair correlation clustering")

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)