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 "singular persistent homology with effective concurrent computation"):
  Abstract: Persistent homology is a popular tool in Topological Data Analysis. It provides numerical characteristics which reflect global geometric properties of data sets. In order to be useful in practice, for example for feature generation in machine learning, it needs to be effectively computable. Classical homology is a computable topological invariant because of the Mayer-Vietoris exact and spectral sequences associated to intrinsic coverings of a space. Regrettably, persistent homology itself is defined through morphing simplicial nerves of coverings of the data set. #R##N#This note introduces a counterpart, the singular persistent homology, where the perspective is different. The data set is left stationary while the parameter is allowed to change in the form of the size of singular simplices. Because of this nature, coverings of the data set are much easier to handle than in other attempts to parallelize the computation of persistent homology. #R##N#When computed directly, which is possible in finite metric spaces, the complexity is certainly worse than for persistent homology but not as much as one would fear. We show however that the singular and the traditional persistent homologies are isomorphic, so it is possible to perform the concurrent computations using traditional algorithms. The important point is that the advantages of distributed computation are not necessarily in speed improvement but in sheer feasibility for large data sets.

Includes members:
  Member "multicore homology via mayer vietoris":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an efficient data retrieval parallel reeb graph algorithm")
  Member "distributed computation of persistent homology":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pi net a deep learning approach to extract topological persistence images")
  Member "a spectral sequence for parallelized persistence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an efficient data retrieval parallel reeb graph algorithm")

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)