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: "statistical analysis and parameter selection for mapper"
  Abstract: In this article, we study the question of the statistical convergence of the 1-dimensional Mapper to its continuous analogue, the Reeb graph. We show that the Mapper is an optimal estimator of the Reeb graph, which gives, as a byproduct, a method to automatically tune its parameters and compute confidence regions on its topological features, such as its loops and flares. This allows to circumvent the issue of testing a large grid of parameters and keeping the most stable ones in the brute-force setting, which is widely used in visualization, clustering and feature selection with the Mapper.

Connected through hyperedge HE-1 (induced by source paper "deep graph mapper seeing graphs through the neural lens"):
  with members:
    - "mincut pooling in graph neural networks"
    - "asap adaptive structure aware pooling for learning hierarchical graph representations"
    - "graph convolutional networks with eigenpooling"
    - "clique pooling for graph classification"
    - "graph u nets"
    - "on graph classification networks datasets and baselines"
    - "self attention graph pooling"
    - "graph filtration learning"

Connected through hyperedge HE-2 (induced by source paper "shapevis high dimensional data visualization at scale"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "line large scale information network embedding"
    - "distributed representations of words and phrases and their compositionality"
    - "representation learning a review and new perspectives"
    - "defining and evaluating network communities based on ground truth"
    - "generalized louvain method for community detection in large networks"

Connected through hyperedge HE-3 (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"
    - "local equivalence and intrinsic metrics between reeb graphs"
    - "convergence between categorical representations of reeb space and mapper"
    - "categorified reeb graphs"
    - "the edit distance for reeb graphs of surfaces"
    - "measuring distance between reeb graphs"

Connected through hyperedge HE-4 (induced by source paper "mima mapper induced manifold alignment for semi supervised fusion of optical image and polarimetric sar data"):
  with members:
    - "orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency channel features"
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "cospace common subspace learning from hyperspectral multispectral correspondences"
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "joint progressive learning from high dimensional data for multi label classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"

Connected through hyperedge HE-5 (induced by source paper "jaccard filtration and stable paths in the mapper"):
  with members:
    - "understanding and predicting links in graphs a persistent homology perspective"
    - "a quest to unravel the metric structure behind perturbed networks"
    - "interesting paths in the mapper"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "mutiscale mapper a framework for topological summarization of data and maps"
    - "fast evaluation of union intersection expressions"

Connected through hyperedge HE-6 (induced by source paper "mapper on graphs for network visualization"):
  with members:
    - "persistent homology guided force directed graph layouts"
    - "topological analysis of nerves reeb spaces mappers and multiscale mappers"
    - "visual detection of structural changes in time varying graphs using persistent homology"
    - "convergence between categorical representations of reeb space and mapper"
    - "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-7 (induced by source paper "topological data analysis of single cell hi c contact maps"):
  with members:
    - "convergence between categorical representations of reeb space and mapper"

Connected through hyperedge HE-8 (induced by source paper "interesting paths in the mapper"):
  with members:
    - "a data structure for nearest common ancestors with linking"
    - "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)