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: "a spectral sequence for parallelized persistence"
  Abstract: We approach the problem of the computation of persistent homology for large datasets by a divide-and-conquer strategy. Dividing the total space into separate but overlapping components, we are able to limit the total memory residency for any part of the computation, while not degrading the overall complexity much. Locally computed persistence information is then merged from the components and their intersections using a spectral sequence generalizing the Mayer-Vietoris long exact sequence. #R##N#We describe the Mayer-Vietoris spectral sequence and give details on how to compute with it. This allows us to merge local homological data into the global persistent homology. Furthermore, we detail how the classical topology constructions inherent in the spectral sequence adapt to a persistence perspective, as well as describe the techniques from computational commutative algebra necessary for this extension. #R##N#The resulting computational scheme suggests a parallelization scheme, and we discuss the communication steps involved in this scheme. Furthermore, the computational scheme can also serve as a guideline for which parts of the boundary matrix manipulation need to co-exist in primary memory at any given time allowing for stratified memory access in single-core computation. The spectral sequence viewpoint also provides easy proofs of a homology nerve lemma as well as a persistent homology nerve lemma. In addition, the algebraic tools we develop to approch persistent homology provide a purely algebraic formulation of kernel, image and cokernel persistence (D. Cohen-Steiner, H. Edelsbrunner, J. Harer, and D. Morozov. Persistent homology for kernels, images, and cokernels. In Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 1011-1020. Society for Industrial and Applied Mathematics, 2009.)

Connected through hyperedge HE-1 (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"
    - "convergence between categorical representations of reeb space and mapper"
    - "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"
    - "towards persistence based reconstruction in euclidean spaces"

Connected through hyperedge HE-2 (induced by source paper "singular persistent homology with effective concurrent computation"):
  with members:
    - "multicore homology via mayer vietoris"
    - "distributed computation of persistent homology"

Connected through hyperedge HE-3 (induced by source paper "multicore homology via mayer vietoris"):
  with members:
    - "yet another graph partitioning problem is np hard"

Connected through hyperedge HE-4 (induced by source paper "distributed computation of persistent homology"):
  with members:
    - "the compressed annotation matrix an efficient data structure for computing persistent cohomology"

Connected through hyperedge HE-5 (induced by source paper "persistence modules algebra and algorithms"):
  with members:
    - "classification of hepatic lesions using the matching metric"

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)