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: "applications of zigzag persistence to topological data analysis"
  Abstract: The theory of zigzag persistence is a substantial extension of persistent homology, and its development has enabled the investigation of several unexplored avenues in the area of topological data analysis. In this paper, we discuss three applications of zigzag persistence: topological bootstrapping, parameter thresholding, and the comparison of witness complexes. The newly emerging area of topological data analysis attempts to use techniques from algebraic topology to study qualitative properties of datasets. Its applicability has been demonstrated in areas as diverse as object recognition, sensor networks, and bioinformatics (Car09). The need for a topological approach to data analysis tasks is justied

Connected through hyperedge HE-1 (induced by source paper "persistent homology over directed acyclic graphs"):
  with members:
    - "distributed computation of persistent homology"
    - "computing multidimensional persistence"

Connected through hyperedge HE-2 (induced by source paper "spectral sparsification of simplicial complexes for clustering and label propagation"):
  with members:
    - "near optimal sketching of low rank tensor regression"
    - "polynomial sized topological approximations using the permutahedron"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "pagerank beyond the web"
    - "graph induced complex on point data"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "computing topological persistence for simplicial maps"
    - "fast approximation of matrix coherence and statistical leverage"

Connected through hyperedge HE-3 (induced by source paper "a geometric perspective on sparse filtrations"):
  with members:
    - "the compressed annotation matrix an efficient data structure for computing persistent cohomology"
    - "efficient and robust persistent homology for measures"
    - "linear size approximations to the vietoris rips filtration"
    - "computing topological persistence for simplicial maps"
    - "towards persistence based reconstruction in euclidean spaces"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-4 (induced by source paper "linear size approximations to the vietoris rips filtration"):
  with members:
    - "towards persistence based reconstruction in euclidean spaces"
    - "fast construction of nets in low dimensional metrics and their applications"

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)