human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "gpu accelerated computation of vietoris rips persistence barcodes"):
  Abstract: The computation of Vietoris-Rips persistence barcodes is both execution-intensive and memory-intensive. In this paper, we study the computational structure of Vietoris-Rips persistence barcodes, and identify several unique mathematical properties and algorithmic opportunities with connections to the GPU. Mathematically and empirically, we look into the properties of apparent pairs, which are independently identifiable persistence pairs comprising up to 99\% of persistence pairs. We give theoretical upper and lower bounds of the apparent pair rate and model the average case. We also design massively parallel algorithms to take advantage of the very large number of simplices that can be processed independently of each other. Having identified these opportunities, we develop a GPU-accelerated software for computing Vietoris-Rips persistence barcodes, called Ripser++. The software achieves up to 30x speedup over the total execution time of the original Ripser and also reduces CPU-memory usage by up to 2.0x. We believe our GPU-acceleration based efforts open a new chapter for the advancement of topological data analysis in the post-Moore's Law era.

Includes members:
  Member "on characterizing the capacity of neural networks using algebraic topology":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "geometry and topology of deep neural networks decision boundaries")
  Member "deep learning with topological signatures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep graph mapper seeing graphs through the neural lens")
  Member "simba an efficient tool for approximating rips filtration persistence via simplicial batch collapse":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "connectivity optimized representation learning via persistent homology")
  Member "a geometric perspective on sparse filtrations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved approximate rips filtrations with shifted integer lattices")
  Member "the simplex tree an efficient data structure for general simplicial complexes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computing discrete morse complexes from simplicial complexes")
  Member "the compressed annotation matrix an efficient data structure for computing persistent cohomology":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "computing persistent homology with various coefficient fields in a single pass")
  Member "computing topological persistence for simplicial maps":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "computing minimal persistent cycles polynomial and hard cases")

Question: Please tell me which class the source paper 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)