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: "principal manifolds and graphs in practice from molecular biology to dynamical systems"
  Abstract: We present several applications of non-linear data modeling, using principal manifolds and principal graphs constructed using the metaphor of elasticity (elastic principal graph approach). These approaches are generalizations of the Kohonen’s self-organizing maps, a class of artificial neural networks. On several examples we show advantages of using non-linear objects for data approximation in comparison to the linear ones. We propose four numerical criteria for comparing linear and non-linear mappings of datasets into the spaces of lower dimension. The examples are taken from comparative political science, from analysis of high-throughput data in molecular biology, from analysis of dynamical systems.

Connected through hyperedge HE-1 (induced by source paper "high dimensional brain in a high dimensional world blessing of dimensionality"):
  with members:
    - "the unreasonable effectiveness of small neural ensembles in high dimensional brain"
    - "blessing of dimensionality at the edge"
    - "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study"
    - "estimating the effective dimension of large biological datasets using fisher separability analysis"
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "stochastic separation theorems"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"

Connected through hyperedge HE-2 (induced by source paper "the unreasonable effectiveness of small neural ensembles in high dimensional brain"):
  with members:
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "augmented artificial intelligence"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "augmented artificial intelligence a conceptual framework"
    - "stochastic separation theorems"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "knowledge transfer between artificial intelligence systems"
    - "approximation with random bases"

Connected through hyperedge HE-3 (induced by source paper "blessing of dimensionality mathematical foundations of the statistical physics of data"):
  with members:
    - "stochastic separation theorems"
    - "stochastic configuration networks fundamentals and algorithms"
    - "knowledge transfer between artificial intelligence systems"
    - "piece wise quadratic approximations of arbitrary error functions for fast and robust machine learning"
    - "approximation with random bases"
    - "caffe convolutional architecture for fast feature embedding"
    - "data complexity measured by principal graphs"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"

Connected through hyperedge HE-4 (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"
    - "topological grammars for data approximation"

Connected through hyperedge HE-5 (induced by source paper "rule mining based classification a benchmark study"):
  with members:
    - "diet networks thin parameters for fat genomic"

Connected through hyperedge HE-6 (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 graphs and manifolds"
    - "robust principal component analysis"
    - "topological grammars for data approximation"

Connected through hyperedge HE-7 (induced by source paper "data complexity measured by principal graphs"):
  with members:
    - "principal graphs and manifolds"
    - "topological grammars for data approximation"

Connected through hyperedge HE-8 (induced by source paper "nonlinear quality of life index"):
  with members:
    - "data visualization in political and social sciences"
    - "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)