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 graphs and manifolds"
  Abstract: In many physical, statistical, biological and other investigations it is desirable to approximate a system of points by objects of lower dimension and/or complexity. For this purpose, Karl Pearson invented principal component analysis in 1901 and found 'lines and planes of closest fit to system of points'. The famous k-means algorithm solves the approximation problem too, but by finite sets instead of lines and planes. This chapter gives a brief practical introduction into the methods of construction of general principal objects, i.e. objects embedded in the 'middle' of the multidimensional data set. As a basis, the unifying framework of mean squared distance approximation of finite datasets is selected. Principal graphs and manifolds are constructed as generalisations of principal components and k-means principal points. For this purpose, the family of expectation/maximisation algorithms with nearest generalisations is presented. Construction of principal graphs with controlled complexity is based on the graph grammar approach.

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"
    - "topological grammars for data approximation"

Connected through hyperedge HE-2 (induced by source paper "ge shu zhi zhi towards deep understanding about worlds"):
  with members:
    - "unsupervised ranking of multi attribute objects based on principal curves"

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

Connected through hyperedge HE-4 (induced by source paper "correction of ai systems by linear discriminants probabilistic foundations"):
  with members:
    - "augmented artificial intelligence"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "augmented artificial intelligence a conceptual framework"
    - "how deep should be the depth of convolutional neural networks a backyard dog case study"
    - "stochastic separation theorems"
    - "stochastic configuration networks fundamentals and algorithms"
    - "neurogenesis deep learning extending deep networks to accommodate new classes"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-5 (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"
    - "robust principal component analysis"
    - "topological grammars for data approximation"

Connected through hyperedge HE-6 (induced by source paper "unsupervised ranking of multi attribute objects based on principal curves"):
  with members:
    - "nonlinear quality of life index"

Connected through hyperedge HE-7 (induced by source paper "data complexity measured by principal graphs"):
  with members:
    - "principal manifolds and graphs in practice from molecular biology to dynamical systems"
    - "topological grammars for data approximation"

Connected through hyperedge HE-8 (induced by source paper "nonlinear quality of life index"):
  with members:
    - "principal manifolds and graphs in practice from molecular biology to dynamical systems"
    - "data visualization in political and social sciences"

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.LG(Machine Learning)