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: "topological grammars for data approximation"
  Abstract: A method of topological grammars is proposed for multidimensional data approximation. For data with complex topology we define a principal cubic complex of low dimension and given complexity that gives the best approximation for the dataset. This complex is a generalization of linear and non-linear principal manifolds and includes them as particular cases. The problem of optimal principal complex construction is transformed into a series of minimization problems for quadratic functionals. These quadratic functionals have a physically transparent interpretation in terms of elastic energy. For the energy computation, the whole complex is represented as a system of nodes and springs. Topologically, the principal complex is a product of one-dimensional continuums (represented by graphs), and the grammars describe how these continuums transform during the process of optimal complex construction. This factorization of the whole process onto one-dimensional transformations using minimization of quadratic energy functionals allows us to construct efficient algorithms.

Connected through hyperedge HE-1 (induced by source paper "estimating the effective dimension of large biological datasets using fisher separability analysis"):
  with members:
    - "the unreasonable effectiveness of small neural ensembles in high dimensional brain"
    - "robust and scalable learning of data manifolds with complex topologies via elpigraph"
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "stochastic separation theorems"
    - "intrinsic dimension estimation of data by principal component analysis"
    - "principal graphs and manifolds"

Connected through hyperedge HE-2 (induced by source paper "robust and scalable learning of complex dataset topologies via elpigraph"):
  with members:
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "stochastic separation theorems"
    - "robust principal graphs for data approximation"
    - "piece wise quadratic approximations of arbitrary error functions for fast and robust machine learning"
    - "data complexity measured by principal graphs"
    - "principal manifolds and graphs in practice from molecular biology to dynamical systems"

Connected through hyperedge HE-3 (induced by source paper "piece wise quadratic approximations of arbitrary error functions for fast and robust machine learning"):
  with members:
    - "smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization"
    - "formal proofs for nonlinear optimization"
    - "vidaexpert user friendly tool for nonlinear visualization and analysis of multidimensional vectorial data"
    - "principal manifolds and graphs in practice from molecular biology to dynamical systems"
    - "principal graphs and manifolds"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "data complexity measured by principal graphs"):
  with members:
    - "principal manifolds and graphs in practice from molecular biology to dynamical systems"
    - "principal graphs and manifolds"

Connected through hyperedge HE-5 (induced by source paper "principal manifolds and graphs in practice from molecular biology to dynamical systems"):
  with members:
    - "principal graphs and manifolds"

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.NE(Neural and Evolutionary Computing)