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: "sliced wasserstein kernel for persistence diagrams"
  Abstract: Persistence diagrams (PDs) play a key role in topological data analysis (TDA), in which they are routinely used to describe topological properties of complicated shapes. PDs enjoy strong stability properties and have proven their utility in various learning contexts. They do not, however, live in a space naturally endowed with a Hilbert structure and are usually compared with specific distances, such as the bottleneck distance. To incorporate PDs in a learning pipeline, several kernels have been proposed for PDs with a strong emphasis on the stability of the RKHS distance w.r.t. perturbations of the PDs. In this article, we use the Sliced Wasserstein approximation SW of the Wasserstein distance to define a new kernel for PDs, which is not only provably stable but also provably discriminative (depending on the number of points in the PDs) w.r.t. the Wasserstein distance $d_1$ between PDs. We also demonstrate its practicality, by developing an approximation technique to reduce kernel computation time, and show that our proposal compares favorably to existing kernels for PDs on several benchmarks.

Connected through hyperedge HE-1 (induced by source paper "understanding the power of persistence pairing via permutation test"):
  with members:
    - "learning metrics for persistence based summaries and applications for graph classification"
    - "on the metric distortion of embedding persistence diagrams into separable hilbert spaces"
    - "a simple yet effective baseline for non attributed graph classification"
    - "deep sets"
    - "on the metric distortion of embedding persistence diagrams into reproducing kernel hilbert spaces"
    - "deep learning with topological signatures"
    - "ricci curvature of the internet topology"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-2 (induced by source paper "progressive wasserstein barycenters of persistence diagrams"):
  with members:
    - "persistence atlas for critical point variability in ensembles"
    - "the topology toolkit"
    - "geometry helps to compare persistence diagrams"
    - "a comparative study of efficient initialization methods for the k means clustering algorithm"
    - "measuring distance between reeb graphs"

Connected through hyperedge HE-3 (induced by source paper "wasserstein exponential kernels"):
  with members:
    - "generalized sliced wasserstein distances"
    - "generative restricted kernel machines"
    - "monge s optimal transport distance for image classification"

Connected through hyperedge HE-4 (induced by source paper "persistent homology as stopping criterion for voronoi interpolation"):
  with members:
    - "the morse theory of cech and delaunay complexes"
    - "delaunay triangulation of manifolds"
    - "the geometric stability of voronoi diagrams with respect to small changes of the sites"

Connected through hyperedge HE-5 (induced by source paper "learning a domain invariant embedding for unsupervised domain adaptation using class conditioned distribution alignment"):
  with members:
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "generative modeling using the sliced wasserstein distance"
    - "image to image translation for domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "few shot adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "stabilizing training of generative adversarial networks through regularization"

Connected through hyperedge HE-6 (induced by source paper "topology preserving deep image segmentation"):
  with members:
    - "a topological regularizer for classifiers via persistent homology"
    - "a deep structured learning approach towards automating connectome reconstruction from 3d electron micrographs"
    - "beyond the pixel wise loss for topology aware delineation"
    - "deep learning with topological signatures"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "on efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms"):
  with members:
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "towards optimal running times for optimal transport"
    - "better and simpler error analysis of the sinkhorn knopp algorithm for matrix scaling"
    - "approximating optimal transport with linear programs"
    - "sparsemax and relaxed wasserstein for topic sparsity"
    - "probabilistic multilevel clustering via composite transportation distance"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (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"
    - "deep learning with topological signatures"
    - "simba an efficient tool for approximating rips filtration persistence via simplicial batch collapse"
    - "learning convolutional neural networks for graphs"

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)