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: "nonlinear approximation via compositions"
  Abstract: Abstract   Given a function dictionary    D    and an approximation budget     N  ∈  N    , nonlinear approximation seeks the linear combination of the best    N    terms        {    T    n    }     1  ≤  n  ≤  N    ⊆  D     to approximate a given function    f    with the minimum approximation error         e    L  ,  f    ≔    min     {    g    n    }   ⊆  R  ,   {    T    n    }   ⊆  D    ‖  f   (  x  )   −    ∑    n  =  1    N      g    n      T    n     (  x  )   ‖  .      Motivated by recent success of deep learning, we propose dictionaries with functions in a form of compositions, i.e.,       T   (  x  )   =    T     (  L  )     ∘    T     (  L  −  1  )     ∘  ⋯  ∘    T     (  1  )      (  x  )       for all     T  ∈  D    , and implement    T    using ReLU feed-forward neural networks (FNNs) with    L    hidden layers. We further quantify the improvement of the best    N   -term approximation rate in terms of    N    when    L    is increased from 1 to 2 or 3 to show the power of compositions. In the case when     L  >  3    , our analysis shows that increasing    L    cannot improve the approximation rate in terms of    N   .  In particular, for any function    f    on     [  0  ,  1  ]    , regardless of its smoothness and even the continuity, if    f    can be approximated using a dictionary when     L  =  1     with the best    N   -term approximation rate       e    L  ,  f    =  O   (    N    −  η    )     , we show that dictionaries with     L  =  2     can improve the best    N   -term approximation rate to       e    L  ,  f    =  O   (    N    −  2  η    )     . We also show that for Holder continuous functions of order    α    on       [  0  ,  1  ]     d     , the application of a dictionary with     L  =  3     in nonlinear approximation can achieve an essentially tight best    N   -term approximation rate       e    L  ,  f    =  O   (    N    −  2  α  ∕  d    )     . Finally, we show that dictionaries consisting of wide FNNs with a few hidden layers are more attractive in terms of computational efficiency than dictionaries with narrow and very deep FNNs for approximating Holder continuous functions if the number of computer cores is larger than    N    in parallel computing.

Connected through hyperedge HE-1 (induced by source paper "deep network approximation for smooth functions"):
  with members:
    - "exponential convergence of the deep neural network approximation for analytic functions"
    - "construction of neural networks for realization of localized deep learning"
    - "universality of deep convolutional neural networks"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "optimal approximation with sparsely connected deep neural networks"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"

Connected through hyperedge HE-2 (induced by source paper "deep learning via dynamical systems an approximation perspective"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "barron spaces and the compositional function spaces for neural network models"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "a priori estimates of the population risk for residual networks"
    - "approximation capabilities of neural ordinary differential equations"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
    - "monge amp ere flow for generative modeling"

Connected through hyperedge HE-3 (induced by source paper "variational physics informed neural networks for solving partial differential equations"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "function approximation by deep networks"
    - "locally adaptive activation functions with slope recovery term for deep and physics informed neural networks"
    - "barron spaces and the compositional function spaces for neural network models"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"
    - "machine learning of linear differential equations using gaussian processes"
    - "deep vs shallow networks an approximation theory perspective"

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)