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: "simba an efficient tool for approximating rips filtration persistence via simplicial batch collapse"
  Abstract: In topological data analysis, a point cloud data P extracted from a metric space is often analyzed by computing the persistence diagram or barcodes of a sequence of Rips complexes built on $P$ indexed by a scale parameter. Unfortunately, even for input of moderate size, the size of the Rips complex may become prohibitively large as the scale parameter increases. Starting with the Sparse Rips filtration introduced by Sheehy, some existing methods aim to reduce the size of the complex so as to improve the time efficiency as well. However, as we demonstrate, existing approaches still fall short of scaling well, especially for high dimensional data. In this paper, we investigate the advantages and limitations of existing approaches. Based on insights gained from the experiments, we propose an efficient new algorithm, called SimBa, for approximating the persistent homology of Rips filtrations with quality guarantees. Our new algorithm leverages a batch collapse strategy as well as a new sparse Rips-like filtration. We experiment on a variety of low and high dimensional data sets. We show that our strategy presents a significant size reduction, and our algorithm for approximating Rips filtration persistence is order of magnitude faster than existing methods in practice.

Connected through hyperedge HE-1 (induced by source paper "gpu accelerated computation of vietoris rips persistence barcodes"):
  with members:
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "deep learning with topological signatures"
    - "a geometric perspective on sparse filtrations"
    - "the simplex tree an efficient data structure for general simplicial complexes"
    - "the compressed annotation matrix an efficient data structure for computing persistent cohomology"
    - "computing topological persistence for simplicial maps"

Connected through hyperedge HE-2 (induced by source paper "connectivity optimized representation learning via persistent homology"):
  with members:
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "a topological regularizer for classifiers via persistent homology"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "ssd single shot multibox detector"
    - "densely connected convolutional networks"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "deep structured energy based models for anomaly detection"

Connected through hyperedge HE-3 (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"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-4 (induced by source paper "on vietoris rips complexes of planar curves"):
  with members:
    - "the persistent homotopy type distance"
    - "computing topological persistence for simplicial maps"
    - "towards persistence based reconstruction in euclidean spaces"

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)