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: "towards persistence based reconstruction in euclidean spaces"
  Abstract: Manifold reconstruction has been extensively studied for the last decade or so, especially in two and three dimensions. Recently, significant improvements were made in higher dimensions, leading to new methods to reconstruct large classes of compact subsets of Euclidean space $\R^d$. However, the complexities of these methods scale up exponentially with d, which makes them impractical in medium or high dimensions, even for handling low-dimensional submanifolds. In this paper, we introduce a novel approach that stands in-between classical reconstruction and topological estimation, and whose complexity scales up with the intrinsic dimension of the data. Specifically, when the data points are sufficiently densely sampled from a smooth $m$-submanifold of $\R^d$, our method retrieves the homology of the submanifold in time at most $c(m)n^5$, where $n$ is the size of the input and $c(m)$ is a constant depending solely on $m$. It can also provably well handle a wide range of compact subsets of $\R^d$, though with worse complexities. Along the way to proving the correctness of our algorithm, we obtain new results on \v{C}ech, Rips, and witness complex filtrations in Euclidean spaces.

Connected through hyperedge HE-1 (induced by source paper "time series featurization via topological data analysis"):
  with members:
    - "deep learning for time series classification a review"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "a short survey of topological data analysis in time series and systems analysis"
    - "quasi periodicity quantification in video data using topology"
    - "persistent homology of attractors for action recognition"
    - "introduction to the r package tda"
    - "an introductory study on time series modeling and forecasting"

Connected through hyperedge HE-2 (induced by source paper "an efficient data retrieval parallel reeb graph algorithm"):
  with members:
    - "mapper on graphs for network visualization"
    - "topological analysis of nerves reeb spaces mappers and multiscale mappers"
    - "convergence between categorical representations of reeb space and mapper"
    - "segmenting a surface mesh into pants using morse theory"
    - "a mountaintop view requires minimal sorting a faster contour tree algorithm"
    - "multicore homology via mayer vietoris"
    - "a spectral sequence for parallelized persistence"

Connected through hyperedge HE-3 (induced by source paper "dtm based filtrations"):
  with members:
    - "efficient and robust persistent homology for measures"
    - "geometric inference on kernel density estimates"
    - "linear size approximations to the vietoris rips filtration"

Connected through hyperedge HE-4 (induced by source paper "on vietoris rips complexes of planar curves"):
  with members:
    - "the persistent homotopy type distance"
    - "simba an efficient tool for approximating rips filtration persistence via simplicial batch collapse"
    - "computing topological persistence for simplicial maps"

Connected through hyperedge HE-5 (induced by source paper "improved topological approximations by digitization"):
  with members:
    - "strong collapse for persistence"
    - "improved approximate rips filtrations with shifted integer lattices"
    - "computing topological persistence for simplicial maps"

Connected through hyperedge HE-6 (induced by source paper "the persistent homology of cyclic graphs"):
  with members:
    - "linear size approximations to the vietoris rips filtration"
    - "computing topological persistence for simplicial maps"

Connected through hyperedge HE-7 (induced by source paper "strong collapse for persistence"):
  with members:
    - "recognition of collapsible complexes is np complete"

Connected through hyperedge HE-8 (induced by source paper "adaptive metrics for adaptive samples"):
  with members:
    - "approximating nearest neighbor distances"

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)