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: "efficient and robust persistent homology for measures"
  Abstract: We extend the notion of the distance to a measure from Euclidean space to probability measures on general metric spaces as a way to do topological data analysis in a way that is robust to noise and outliers. We then give an efficient way to approximate the sub-level sets of this function by a union of metric balls and extend previous results on sparse Rips filtrations to this setting. This robust and efficient approach to topological data analysis is illustrated with several examples from an implementation.

Connected through hyperedge HE-1 (induced by source paper "dtm based filtrations"):
  with members:
    - "geometric inference on kernel density estimates"
    - "linear size approximations to the vietoris rips filtration"
    - "towards persistence based reconstruction in euclidean spaces"

Connected through hyperedge HE-2 (induced by source paper "persistent homology for low complexity models"):
  with members:
    - "isometric sketching of any set via the restricted isometry property"
    - "practical and optimal lsh for angular distance"
    - "the simplex tree an efficient data structure for general simplicial complexes"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "local doubling dimension of point sets"
    - "a unified framework for linear dimensionality reduction in l1"

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"
    - "linear size approximations to the vietoris rips filtration"
    - "computing topological persistence for simplicial maps"
    - "applications of zigzag persistence to topological data analysis"
    - "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 "topological analysis of scalar fields with outliers"):
  with members:
    - "topological de noising strengthening the topological signal"
    - "towards persistence based reconstruction in euclidean spaces"

Connected through hyperedge HE-5 (induced by source paper "geometric inference on kernel density estimates"):
  with members:
    - "introduction to the r package tda"
    - "linear size approximations to the vietoris rips filtration"
    - "a gentle introduction to the kernel distance"
    - "witnessed k distance"
    - "boundary measures for geometric inference"
    - "computing multidimensional persistence"
    - "stability of curvature measures"
    - "towards persistence based reconstruction in euclidean spaces"

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)