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: "persistence images an alternative persistent homology representation"
  Abstract: Many datasets can be viewed as a noisy sampling of an underlying topological space. Topological data analysis aims to understand and exploit this underlying structure for the purpose of knowledge discovery. A fundamental tool of the discipline is persistent homology, which captures underlying data-driven, scale-dependent homological information. A representation in a "persistence diagram" concisely summarizes this information. By giving the space of persistence diagrams a metric structure, a class of effective machine learning techniques can be applied. We modify the persistence diagram to a "persistence image" in a manner that allows the use of a wider set of distance measures and extends the list of tools from machine learning which can be utilized. It is shown that several machine learning techniques, applied to persistence images for classification tasks, yield high accuracy rates on multiple data sets. Furthermore, these same machine learning techniques fare better when applied to persistence images than when applied to persistence diagrams. We discuss sensitivity of the classification accuracy to the parameters associated to the approach. An application of persistence image based classification to a data set arising from applied dynamical systems is presented to further illustrate.

Connected through hyperedge HE-1 (induced by source paper "a persistence landscapes toolbox for topological statistics"):
  with members:
    - "persistence images a stable vector representation of persistent homology"
    - "introduction to the r package tda"
    - "topological and statistical behavior classifiers for tracking 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)