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: "linear size approximations to the vietoris rips filtration"
  Abstract: The Vietoris-Rips filtration is a versatile tool in topological data analysis. It is a sequence of simplicial complexes built on a metric space to add topological structure to an otherwise disconnected set of points. It is widely used because it encodes useful information about the topology of the underlying metric space. This information is often extracted from its so-called persistence diagram. Unfortunately, this filtration is often too large to construct in full. We show how to construct an O(n)-size filtered simplicial complex on an $n$-point metric space such that its persistence diagram is a good approximation to that of the Vietoris-Rips filtration. This new filtration can be constructed in $O(n\log n)$ time. The constant factors in both the size and the running time depend only on the doubling dimension of the metric space and the desired tightness of the approximation. For the first time, this makes it computationally tractable to approximate the persistence diagram of the Vietoris-Rips filtration across all scales for large data sets. #R##N#We describe two different sparse filtrations. The first is a zigzag filtration that removes points as the scale increases. The second is a (non-zigzag) filtration that yields the same persistence diagram. Both methods are based on a hierarchical net-tree and yield the same guarantees.

Connected through hyperedge HE-1 (induced by source paper "topological autoencoders"):
  with members:
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "graph filtration learning"
    - "homology preserving dimensionality reduction via manifold landmarking and tearing"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "improved topological approximations by digitization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "learning metrics for persistence based summaries and applications for graph classification"):
  with members:
    - "understanding and predicting links in graphs a persistent homology perspective"
    - "deep sets"
    - "how powerful are graph neural networks"
    - "covariant compositional networks for learning graphs"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "sliced wasserstein kernel for persistence diagrams"
    - "deep learning with topological signatures"
    - "simba an efficient tool for approximating rips filtration persistence via simplicial batch collapse"

Connected through hyperedge HE-3 (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"
    - "graph induced complex on point data"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-4 (induced by source paper "dtm based filtrations"):
  with members:
    - "efficient and robust persistent homology for measures"
    - "geometric inference on kernel density estimates"
    - "towards persistence based reconstruction in euclidean spaces"

Connected through hyperedge HE-5 (induced by source paper "the persistent homology of cyclic graphs"):
  with members:
    - "computing topological persistence for simplicial maps"
    - "towards persistence based reconstruction in euclidean spaces"

Connected through hyperedge HE-6 (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-7 (induced by source paper "polynomial sized topological approximations using the permutahedron"):
  with members:
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "computing topological persistence for simplicial maps"

Connected through hyperedge HE-8 (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"
    - "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"

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)