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: "convergence between categorical representations of reeb space and mapper"
  Abstract: The Reeb space, which generalizes the notion of a Reeb graph, is one of the few tools in topological data analysis and visualization suitable for the study of multivariate scientific datasets. First introduced by Edelsbrunner et al., it compresses the components of the level sets of a multivariate mapping and obtains a summary representation of their relationships. A related construction called mapper, and a special case of the mapper construction called the Joint Contour Net have been shown to be effective in visual analytics. Mapper and JCN are intuitively regarded as discrete approximations of the Reeb space, however without formal proofs or approximation guarantees. An open question has been proposed by Dey et al. as to whether the mapper construction converges to the Reeb space in the limit. #R##N##R##N#In this paper, we are interested in developing the theoretical understanding of the relationship between the Reeb space and its discrete approximations to support its use in practical data analysis. Using tools from category theory, we formally prove the convergence between the Reeb space and mapper in terms of an interleaving distance between their categorical representations. Given a sequence of refined discretizations, we prove that these approximations converge to the Reeb space in the interleaving distance; this also helps to quantify the approximation quality of the discretization at a fixed resolution.

Connected through hyperedge HE-1 (induced by source paper "topoact exploring the shape of activations in deep learning"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "understanding deep image representations by inverting them"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-2 (induced by source paper "intrinsic interleaving distance for merge trees"):
  with members:
    - "the l cophenetic metric for phylogenetic trees as an interleaving distance"
    - "fpt algorithms for computing gromov hausdorff and interleaving distances between trees"
    - "statistical analysis and parameter selection for mapper"
    - "local equivalence and intrinsic metrics between reeb graphs"
    - "categorified reeb graphs"
    - "the edit distance for reeb graphs of surfaces"
    - "measuring distance between reeb graphs"

Connected through hyperedge HE-3 (induced by source paper "the l cophenetic metric for phylogenetic trees as an interleaving distance"):
  with members:
    - "computational complexity of the interleaving distance"
    - "categorified reeb graphs"
    - "binary adder circuits of asymptotically minimum depth linear size and fan out two"
    - "computing the gromov hausdorff distance for metric trees"
    - "the edit distance for reeb graphs of surfaces"
    - "measuring distance between reeb graphs"

Connected through hyperedge HE-4 (induced by source paper "sheaf theoretic stratification learning from geometric and topological perspectives"):
  with members:
    - "sheaf theoretic stratification learning"
    - "generalized principal component analysis gpca"
    - "approximating local homology from samples"

Connected through hyperedge HE-5 (induced by source paper "mapper on graphs for network visualization"):
  with members:
    - "persistent homology guided force directed graph layouts"
    - "statistical analysis and parameter selection for mapper"
    - "topological analysis of nerves reeb spaces mappers and multiscale mappers"
    - "visual detection of structural changes in time varying graphs using persistent homology"
    - "a note on the pagerank of undirected graphs"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "defining and evaluating network communities based on ground truth"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "an efficient data retrieval parallel reeb graph algorithm"):
  with members:
    - "mapper on graphs for network visualization"
    - "topological analysis of nerves reeb spaces mappers and multiscale mappers"
    - "segmenting a surface mesh into pants using morse theory"
    - "a mountaintop view requires minimal sorting a faster contour tree algorithm"
    - "multicore homology via mayer vietoris"
    - "a spectral sequence for parallelized persistence"
    - "towards persistence based reconstruction in euclidean spaces"

Connected through hyperedge HE-7 (induced by source paper "topological data analysis of single cell hi c contact maps"):
  with members:
    - "statistical analysis and parameter selection for mapper"

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)