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 "deep learning via dynamical systems an approximation perspective"):
  Abstract: We build on the dynamical systems approach to deep learning, where deep residual networks are idealized as continuous-time dynamical systems. Although theoretical foundations have been developed on the optimization side through mean-field optimal control theory, the function approximation properties of such models remain largely unexplored, especially when the dynamical systems are controlled by functions of low complexity. In this paper, we establish some basic results on the approximation capabilities of deep learning models in the form of dynamical systems. In particular, we derive general sufficient conditions for universal approximation of functions in $L^p$ using flow maps of dynamical systems, and we also deduce some results on their approximation rates for specific cases. Overall, these results reveal that composition function approximation through flow maps present a new paradigm in approximation theory and contributes to building a useful mathematical framework to investigate deep learning.

Includes members:
  Member "nonlinear approximation and deep relu networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hp vpinns variational physics informed neural networks with domain decomposition")
  Member "nonlinear approximation via compositions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep network approximation for smooth functions")
  Member "barron spaces and the compositional function spaces for neural network models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a corrective view of neural networks representation memorization and learning")
  Member "understanding and improving transformer from a multi particle dynamic system point of view":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differential dynamic programming neural optimizer")
  Member "a priori estimates of the population risk for residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning the mapping mathbf x mapsto sum_ i 1 d x_i 2 the cost of finding the needle in a haystack")
  Member "approximation capabilities of neural ordinary differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dissecting neural odes")
  Member "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "randomization matters how to defend against strong adversarial attacks")
  Member "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "understanding and improving transformer from a multi particle dynamic system point of view")

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)