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: "geometry helps to compare persistence diagrams"
  Abstract: Exploiting geometric structure to improve the asymptotic complexity of discrete assignment problems is a well-studied subject. In contrast, the practical advantages of using geometry for such problems have not been explored. We implement geometric variants of the Hopcroft--Karp algorithm for bottleneck matching (based on previous work by Efrat el al.) and of the auction algorithm by Bertsekas for Wasserstein distance computation. Both implementations use k-d trees to replace a linear scan with a geometric proximity query. Our interest in this problem stems from the desire to compute distances between persistence diagrams, a problem that comes up frequently in topological data analysis. We show that our geometric matching algorithms lead to a substantial performance gain, both in running time and in memory consumption, over their purely combinatorial counterparts. Moreover, our implementation significantly outperforms the only other implementation available for comparing persistence diagrams.

Connected through hyperedge HE-1 (induced by source paper "progressive wasserstein barycenters of persistence diagrams"):
  with members:
    - "persistence atlas for critical point variability in ensembles"
    - "the topology toolkit"
    - "sliced wasserstein kernel for persistence diagrams"
    - "a comparative study of efficient initialization methods for the k means clustering algorithm"
    - "measuring distance between reeb graphs"

Connected through hyperedge HE-2 (induced by source paper "learning metrics for persistence based summaries and applications for graph classification"):
  with members:
    - "understanding and predicting links in graphs a persistent homology perspective"
    - "deep sets"
    - "how powerful are graph neural networks"
    - "covariant compositional networks for learning graphs"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "sliced wasserstein kernel for persistence diagrams"
    - "deep learning with topological signatures"
    - "simba an efficient tool for approximating rips filtration persistence via simplicial batch collapse"

Connected through hyperedge HE-3 (induced by source paper "understanding the topology and the geometry of the persistence diagram space via optimal partial transport"):
  with members:
    - "perturbation robust representations of topological persistence diagrams"
    - "the density of expected persistence diagrams and its kernel based estimation"
    - "sliced wasserstein kernel for persistence diagrams"
    - "sliced wasserstein kernels for probability distributions"
    - "robust statistics hypothesis testing and confidence intervals for persistent homology on metric measure spaces"
    - "graph kernels between point clouds"

Connected through hyperedge HE-4 (induced by source paper "statistical parameter selection for clustering persistence diagrams"):
  with members:
    - "progressive wasserstein barycenters of persistence diagrams"
    - "task based augmented contour trees with fibonacci heaps"
    - "persistence atlas for critical point variability in ensembles"
    - "the topology toolkit"

Connected through hyperedge HE-5 (induced by source paper "persistence b spline grids stable vector representation of persistence diagrams based on data fitting"):
  with members:
    - "on the metric distortion of embedding persistence diagrams into separable hilbert spaces"
    - "sliced wasserstein kernel for persistence diagrams"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-6 (induced by source paper "on computing a center persistence diagram"):
  with members:
    - "approximating k ell center clustering for curves"
    - "approximate nearest neighbors in the space of persistence diagrams"
    - "on the closest string and substring problems"

Connected through hyperedge HE-7 (induced by source paper "a topological reading lesson classification of mnist using tda"):
  with members:
    - "persistent entropy a scale invariant topological statistic for analyzing cell arrangements"
    - "a persistence landscapes toolbox for topological statistics"

Connected through hyperedge HE-8 (induced by source paper "efficient approximation of the matching distance for 2 parameter persistence"):
  with members:
    - "computing the interleaving distance is np hard"
    - "the rank invariant stability via interleavings"

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)