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: "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.

Connected through hyperedge HE-1 (induced by source paper "random projections and sampling algorithms for clustering of high dimensional polygonal curves"):
  with members:
    - "the one way communication complexity of dynamic time warping distance"
    - "walking the dog fast in practice algorithm engineering of the fr echet distance"
    - "beyond worst case analysis"
    - "on coresets for logistic regression"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "clustering is difficult only when it does not matter"

Connected through hyperedge HE-2 (induced by source paper "hardness of consensus problems for circular strings and time series averaging"):
  with members:
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "exact mean computation in dynamic time warping spaces"
    - "dynamic time warping and geometric edit distance breaking the quadratic barrier"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "on the closest string and substring problems"

Connected through hyperedge HE-3 (induced by source paper "tight hardness results for consensus problems on circular strings and time series"):
  with members:
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "exact mean computation in dynamic time warping spaces"
    - "dynamic time warping and geometric edit distance breaking the quadratic barrier"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "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)