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 "skeletonisation algorithms for unorganised point clouds with theoretical guarantees"):
  Abstract: Real datasets often come in the form of an unorganised cloud of points. An overall shape of such data is hard to visualise when data points are given by coordinates in a high-dimensional Euclidean space. More general data can be represented by an abstract graph with weights or distances on links between data points. The skeletonisation problem for a point cloud is to find a graph or a skeleton that correctly represents the shape of a cloud. This paper compares three algorithms that solve the data skeletonisation problem for a general cloud with topological and geometric guarantees. First, the Mapper algorithm outputs a network of interlinked clusters. Second, the $\alpha$-Reeb algorithm discretises the classical Reeb graph and can be applied to discrete clouds at different scales $\alpha$. The third algorithm HoPeS produces a Homologically Persistent Skeleton without any extra parameters. HoPeS represents the 1-dimensional shape of a cloud at any scale by the Optimality Theorem. The Reconstruction Theorem gives conditions on a noisy sample of a graph when HoPeS provides a reconstructed graph with a correct homotopy type and within a small offset of the sample. The experiments on synthetic and real data show that HoPeS outperforms the other algorithms on several topological and geometric measures.

Includes members:
  Member "a fast and robust algorithm to count topologically persistent holes in noisy clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "skeletonisation algorithms with theoretical guarantees for unorganised point clouds with high levels of noise")
  Member "graph induced complex on point data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "topological data analysis with epsilon net induced lazy witness complex")

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)