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 "geometric inference on kernel density estimates"):
  Abstract: We show that geometric inference of a point cloud can be calculated by examining its kernel density estimate with a Gaussian kernel. This allows one to consider kernel density estimates, which are robust to spatial noise, subsampling, and approximate computation in comparison to raw point sets. This is achieved by examining the sublevel sets of the kernel distance, which isomorphically map to superlevel sets of the kernel density estimate. We prove new properties about the kernel distance, demonstrating stability results and allowing it to inherit reconstruction results from recent advances in distance-based topological reconstruction. Moreover, we provide an algorithm to estimate its topology using weighted Vietoris-Rips complexes.

Includes members:
  Member "introduction to the r package tda":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient topological layer based on persistent landscapes")
  Member "efficient and robust persistent homology for measures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dtm based filtrations")
  Member "linear size approximations to the vietoris rips filtration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "topological autoencoders")
  Member "a gentle introduction to the kernel distance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the gaussiansketch for almost relative error kernel distance")
  Member "witnessed k distance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lower bounds for k distance approximation")
  Member "boundary measures for geometric inference":
    also connected through other hyperedges:
      (none)
  Member "computing multidimensional persistence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "persistent homology as stopping criterion for natural neighbor interpolation")
  Member "stability of curvature measures":
    also connected through other hyperedges:
      (none)

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)