human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "finding the mode of a kernel density estimate"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "optimal terminal dimensionality reduction in euclidean space"
    - "improved coresets for kernel density estimates"
    - "coresets for kernel regression"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "estimating continuous distributions in bayesian classifiers"
    - "union of random minkowski sums and network vulnerability analysis"

Connected through hyperedge HE-2 (induced by source paper "persistence atlas for critical point variability in ensembles"):
  with members:
    - "the topology toolkit"
    - "computational complexity of the interleaving distance"
    - "sliced wasserstein kernel for persistence diagrams"
    - "measuring distance between reeb graphs"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "near optimal coresets of kernel density estimates"):
  with members:
    - "improved coresets for kernel density estimates"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "on the equivalence between herding and conditional gradient algorithms"
    - "a gentle introduction to the kernel distance"
    - "algorithms for eps approximations of terrains"

Connected through hyperedge HE-4 (induced by source paper "dtm based filtrations"):
  with members:
    - "efficient and robust persistent homology for measures"
    - "linear size approximations to the vietoris rips filtration"
    - "towards persistence based reconstruction in euclidean spaces"

Connected through hyperedge HE-5 (induced by source paper "improved coresets for kernel density estimates"):
  with members:
    - "algorithmic discrepancy beyond partial coloring"
    - "a gentle introduction to the kernel distance"

Connected through hyperedge HE-6 (induced by source paper "convergence between categorical representations of reeb space and mapper"):
  with members:
    - "categorified reeb graphs"
    - "mutiscale mapper a framework for topological summarization of data and maps"

Question: Please tell me which class the center node 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)