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 "dissecting neural odes"):
  Abstract: Continuous deep learning architectures have recently re-emerged as variants of Neural Ordinary Differential Equations (Neural ODEs). The infinite-depth approach offered by these models theoretically bridges the gap between deep learning and dynamical systems; however, deciphering their inner working is still an open challenge and most of their applications are currently limited to the inclusion as generic black-box modules. In this work, we "open the box" and offer a system-theoretic perspective, including state augmentation strategies and robustness, with the aim of clarifying the influence of several design choices on the underlying dynamics. We also introduce novel architectures: among them, a Galerkin-inspired depth-varying parameter model and neural ODEs with data-controlled vector fields.

Includes members:
  Member "universal differential equations for scientific machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stable neural flows")
  Member "imexnet a forward stable deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep connections between learning from limited labels physical parameter estimation inspiration for regularization")
  Member "anodev2 a coupled neural ode evolution framework":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep connections between learning from limited labels physical parameter estimation inspiration for regularization")
  Member "deep learning via dynamical systems an approximation perspective":
    also connected through other hyperedges:
      (none)
  Member "state space representations of deep neural networks":
    also connected through other hyperedges:
      (none)
  Member "neural jump stochastic differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")
  Member "neural stochastic differential equations deep latent gaussian models in the diffusion limit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable gradients for stochastic differential equations")
  Member "deep equilibrium models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory")

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)