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: "approximating continuous functions on persistence diagrams using template functions"
  Abstract: The persistence diagram is an increasingly useful tool arising from the field of Topological Data Analysis. However, using these diagrams in conjunction with machine learning techniques requires some mathematical finesse. The most success to date has come from finding methods for turning persistence diagrams into vectors in $\mathbb{R}^n$ in a way which preserves as much of the space of persistence diagrams as possible, commonly referred to as featurization. In this paper, we describe a mathematical framework for featurizing the persistence diagram space using template functions. These functions are general as they are only required to be continuous, have a compact support, and separate points. We discuss two example realizations of these functions: tent functions and Chybeyshev interpolating polynomials. Both of these functions are defined on a grid superposed on the birth-lifetime plane. We then combine the resulting features with machine learning algorithms to perform supervised classification and regression on several example data sets, including manifold data, shape data, and an embedded time series from a Rossler system. Our results show that the template function approach yields high accuracy rates that match and often exceed the results of existing methods for featurizing persistence diagrams. One counter-intuitive observation is that in most cases using interpolating polynomials, where each point contributes globally to the feature vector, yields significantly better results than using tent functions, where the contribution of each point is localized to its grid cell. Along the way, we also provide a complete characterization of compact sets in persistence diagram space endowed with the bottleneck distance.

Connected through hyperedge HE-1 (induced by source paper "adaptive template systems data driven feature selection for learning with persistence diagrams"):
  with members:
    - "shape retrieval of non rigid 3d human models"
    - "supervised learning of labeled pointcloud differences via cover tree entropy reduction"
    - "a persistence landscapes toolbox for topological statistics"

Connected through hyperedge HE-2 (induced by source paper "adaptive partitioning for template functions on persistence diagrams"):
  with members:
    - "shape retrieval of non rigid 3d human models"
    - "sliced wasserstein kernel for persistence diagrams"

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)