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 "random projections and sampling algorithms for clustering of high dimensional polygonal curves"):
  Abstract: We study the center and median clustering problems for high-dimensional polygonal curves with finite but unbounded complexity. We tackle the computational issue that arises from the high number of dimensions by defining a Johnson-Lindenstrauss projection for polygonal curves. We analyze the resulting error in terms of the Frechet distance, which is a natural dissimilarity measure for curves. Our algorithms for the median clustering achieve sublinear dependency on the number of input curves via subsampling. For the center clustering we utilize Buchin et al. (2019a) algorithm that achieves linear running-time in the number of input curves. We evaluate our results empirically utilizing a fast, CUDA-parallelized variant of the Alt and Godau algorithm for the Frechet distance. Our experiments show that our clustering algorithms have fast and accurate practical implementations that yield meaningful results on real world data from various physical domains.

Includes members:
  Member "on the hardness of computing an average curve":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hardness of consensus problems for circular strings and time series averaging")
  Member "the one way communication complexity of dynamic time warping distance":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic time warping in strongly subquadratic time algorithms for the low distance regime and approximate evaluation")
  Member "walking the dog fast in practice algorithm engineering of the fr echet distance":
    also connected through other hyperedges:
      (none)
  Member "beyond worst case analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "escaping cannibalization correlation robust pricing for a unit demand buyer")
  Member "on coresets for logistic regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "layered sampling for robust optimization problems")
  Member "turning big data into tiny data constant size coresets for k means pca and projective clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fair clustering with multiple colors")
  Member "strong coresets for k median and subspace approximation goodbye dimension":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coresets for clustering in graphs of bounded treewidth")
  Member "a fast implementation of near neighbors queries for frechet distance gis cup":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sublinear data structures for short fr echet queries")

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.LG(Machine Learning)