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 "understanding the topology and the geometry of the persistence diagram space via optimal partial transport"):
  Abstract: Despite the obvious similarities between the metrics used in topological data analysis and those of optimal transport, an optimal-transport based formalism to study persistence diagrams and similar topological descriptors has yet to come. In this article, by considering the space of persistence diagrams as a measure space, and by observing that its metrics can be expressed as solutions of optimal partial transport problems, we introduce a generalization of persistence diagrams, namely Radon measures supported on the upper half plane. Such measures naturally appear in topological data analysis when considering continuous representations of persistence diagrams (e.g.\ persistence surfaces) but also as limits for laws of large numbers on persistence diagrams or as expectations of probability distributions on the persistence diagrams space. We study the topological properties of this new space, which will also hold for the closed subspace of persistence diagrams. New results include a characterization of convergence with respect to transport metrics, the existence of Fr\'echet means for any distribution of diagrams, and an exhaustive description of continuous linear representations of persistence diagrams. We also showcase the usefulness of this framework to study random persistence diagrams by providing several statistical results made meaningful thanks to this new formalism.

Includes members:
  Member "perturbation robust representations of topological persistence diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pi net a deep learning approach to extract topological persistence images")
  Member "the density of expected persistence diagrams and its kernel based estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sinkhorn divergence of topological signature estimates for time series classification")
  Member "sliced wasserstein kernel for persistence diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding the power of persistence pairing via permutation test")
  Member "geometry helps to compare persistence diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "progressive wasserstein barycenters of persistence diagrams")
  Member "sliced wasserstein kernels for probability distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning autoencoders with relational regularization")
  Member "robust statistics hypothesis testing and confidence intervals for persistent homology on metric measure spaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stabilizing the unstable output of persistent homology computations")
  Member "graph kernels between point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "node masking making graph neural networks generalize and scale better")

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)