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: "graph induced complex on point data"
  Abstract: The efficiency of extracting topological information from point data depends largely on the complex that is built on top of the data points. From a computational viewpoint, the most favored complexes for this purpose have so far been Vietoris-Rips and witness complexes. While the Vietoris-Rips complex is simple to compute and is a good vehicle for extracting topology of sampled spaces, its size is huge--particularly in high dimensions. The witness complex on the other hand enjoys a smaller size because of a subsampling, but fails to capture the topology in high dimensions unless imposed with extra structures. We investigate a complex called the {\em graph induced complex} that, to some extent, enjoys the advantages of both. It works on a subsample but still retains the power of capturing the topology as the Vietoris-Rips complex. It only needs a graph connecting the original sample points from which it builds a complex on the subsample thus taming the size considerably. We show that, using the graph induced complex one can (i) infer the one dimensional homology of a manifold from a very lean subsample, (ii) reconstruct a surface in three dimension from a sparse subsample without computing Delaunay triangulations, (iii) infer the persistent homology groups of compact sets from a sufficiently dense sample. We provide experimental evidences in support of our theory.

Connected through hyperedge HE-1 (induced by source paper "topological data analysis with epsilon net induced lazy witness complex"):
  with members:
    - "introduction to the r package tda"
    - "net and prune a linear time algorithm for euclidean distance problems"
    - "linear size approximations to the vietoris rips filtration"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-2 (induced by source paper "stabilizing the unstable output of persistent homology computations"):
  with members:
    - "supervised learning of labeled pointcloud differences via cover tree entropy reduction"
    - "topological and statistical behavior classifiers for tracking applications"
    - "robust statistics hypothesis testing and confidence intervals for persistent homology on metric measure spaces"

Connected through hyperedge HE-3 (induced by source paper "skeletonisation algorithms for unorganised point clouds with theoretical guarantees"):
  with members:
    - "a fast and robust algorithm to count topologically persistent holes in noisy clouds"

Connected through hyperedge HE-4 (induced by source paper "the inverse voronoi problem in graphs"):
  with members:
    - "on the minimum consistent subset problem"
    - "voronoi diagrams on planar graphs and computing the diameter in deterministic tilde o n 5 3 time"
    - "better tradeoffs for exact distance oracles in planar graphs"
    - "subquadratic algorithms for the diameter and the sum of pairwise distances in planar graphs"
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"
    - "near optimal sample compression for nearest neighbors"
    - "voronoi game on graphs"
    - "minimum weight triangulation is np hard"

Connected through hyperedge HE-5 (induced by source paper "computing discrete morse complexes from simplicial complexes"):
  with members:
    - "the stellar tree a compact representation for simplicial complexes and beyond"
    - "building efficient and compact data structures for simplicial complexes"
    - "the simplex tree an efficient data structure for general simplicial complexes"
    - "reducing complexes in multidimensional persistent homology theory"
    - "the compressed annotation matrix an efficient data structure for computing persistent cohomology"
    - "distributed computation of persistent homology"
    - "random discrete morse theory and a new library of triangulations"

Connected through hyperedge HE-6 (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"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "computing topological persistence for simplicial maps"
    - "applications of zigzag persistence to topological data analysis"
    - "fast approximation of matrix coherence and statistical leverage"

Connected through hyperedge HE-7 (induced by source paper "parameter free topology inference and sparsification for data on manifolds"):
  with members:
    - "linear size approximations to the vietoris rips filtration"
    - "towards persistence based reconstruction in euclidean spaces"

Connected through hyperedge HE-8 (induced by source paper "stabilizing the output of persistent homology computations"):
  with members:
    - "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)