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: "anodev2 a coupled neural ode evolution framework"
  Abstract: It has been observed that residual networks can be viewed as the explicit Euler discretization of an Ordinary Differential Equation (ODE). This observation motivated the introduction of so-called Neural ODEs, which allow more general discretization schemes with adaptive time stepping. Here, we propose ANODEV2, which is an extension of this approach that also allows evolution of the neural network parameters, in a coupled ODE-based formulation. The Neural ODE method introduced earlier is in fact a special case of this new more general framework. We present the formulation of ANODEV2, derive optimality conditions, and implement a coupled reaction-diffusion-advection version of this framework in PyTorch. We present empirical results using several different configurations of ANODEV2, testing them on multiple models on CIFAR-10. We report results showing that this coupled ODE-based framework is indeed trainable, and that it achieves higher accuracy, as compared to the baseline models as well as the recently-proposed Neural ODE approach.

Connected through hyperedge HE-1 (induced by source paper "dissecting neural odes"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "imexnet a forward stable deep neural network"
    - "deep learning via dynamical systems an approximation perspective"
    - "state space representations of deep neural networks"
    - "neural jump stochastic differential equations"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "deep equilibrium models"
    - "port hamiltonian approach to neural network training"

Connected through hyperedge HE-2 (induced by source paper "deep connections between learning from limited labels physical parameter estimation inspiration for regularization"):
  with members:
    - "symmetric block low rank layers for fully reversible multilevel neural networks"
    - "imexnet a forward stable deep neural network"
    - "fully hyperbolic convolutional neural networks"
    - "leanconvnets low cost yet effective convolutional neural networks"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "regularization for deep learning a taxonomy"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"

Connected through hyperedge HE-3 (induced by source paper "interpolated adjoint method for neural odes"):
  with members:
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "infocnf an efficient conditional continuous normalizing flow with adaptive solvers"
    - "spectral normalization for generative adversarial networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "approximation capabilities of neural odes and invertible residual networks"):
  with members:
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "diffeqflux jl a julia library for neural differential equations"
    - "spectral normalization for generative adversarial networks"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "deep residual learning for image recognition"

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)