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 vietoris rips complexes of planar curves"):
  Abstract: A Vietoris--Rips complex is a way to thicken a (possibly discrete) metric space into a larger space containing more topological information. We prove that if $C$ is a convex closed differentiable curve in the plane such that the convex hull of $C$ contains the evolute of $C$, then the homotopy type of a Vietoris--Rips complex of a subset of $n$ points from $C$ can be computed in time $O(n\log n)$. Furthermore, we show how to compute the $k$-dimensional persistent homology of these $n$ points in running time $O(n^2(k+\log n))$, which is nearly quadratic in the number of vertices $n$. This improves upon the traditional persistent homology algorithm, which is cubic in the number of simplices of dimension at most $k+1$, and hence of running time $O(n^{3(k+2)})$ in the number of vertices $n$. We ask if there are other geometric settings in which computing persistent homology is (say) quadratic or cubic in the number of vertices, instead of in the number of simplices.

Includes members:
  Member "the persistent homotopy type distance":
    also connected through other hyperedges:
      (none)
  Member "simba an efficient tool for approximating rips filtration persistence via simplicial batch collapse":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gpu accelerated computation of vietoris rips persistence barcodes")
  Member "computing topological persistence for simplicial maps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gpu accelerated computation of vietoris rips persistence barcodes")
  Member "towards persistence based reconstruction in euclidean spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time series featurization via topological data analysis")

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)