human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "principal manifolds and graphs in practice from molecular biology to dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high dimensional brain in a high dimensional world blessing of dimensionality")
  Member "principal graphs and manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "estimating the effective dimension of large biological datasets using fisher separability analysis")
  Member "topological grammars for data approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating the effective dimension of large biological datasets using fisher separability analysis")

Question: Please tell me which class the source paper 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)