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: "data complexity measured by principal graphs"
  Abstract: Abstract   How to measure the complexity of a finite set of vectors embedded in a multidimensional space? This is a non-trivial question which can be approached in many different ways. Here we suggest a set of data complexity measures using universal approximators, principal cubic complexes. Principal cubic complexes generalize the notion of principal manifolds for datasets with non-trivial topologies. The type of the principal cubic complex is determined by its dimension and a grammar of elementary graph transformations. The simplest grammar produces principal trees.  We introduce three natural types of data complexity: (1) geometric (deviation of the data’s approximator from some “idealized” configuration, such as deviation from harmonicity); (2) structural (how many elements of a principal graph are needed to approximate the data), and (3) construction complexity (how many applications of elementary graph transformations are needed to construct the principal object starting from the simplest one).  We compute these measures for several simulated and real-life data distributions and show them in the “accuracy–complexity” plots, helping to optimize the accuracy/complexity ratio. We discuss various issues connected with measuring data complexity. Software for computing data complexity measures from principal cubic complexes is provided as well.

Connected through hyperedge HE-1 (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-2 (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"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "principal manifolds and graphs in practice from molecular biology to dynamical systems"

Connected through hyperedge HE-3 (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"
    - "principal manifolds and graphs in practice from molecular biology to dynamical systems"
    - "topological grammars for data approximation"

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)