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: "anode unconditionally accurate memory efficient gradients for neural odes"
  Abstract: Residual neural networks can be viewed as the forward Euler discretization of an Ordinary Differential Equation (ODE) with a unit time step. This has recently motivated researchers to explore other discretization approaches and train ODE based networks. However, an important challenge of neural ODEs is their prohibitive memory cost during gradient backpropogation. Recently a method proposed in [8], claimed that this memory overhead can be reduced from O(LN_t), where N_t is the number of time steps, down to O(L) by solving forward ODE backwards in time, where L is the depth of the network. However, we will show that this approach may lead to several problems: (i) it may be numerically unstable for ReLU/non-ReLU activations and general convolution operators, and (ii) the proposed optimize-then-discretize approach may lead to divergent training due to inconsistent gradients for small time step sizes. We discuss the underlying problems, and to address them we propose ANODE, an Adjoint based Neural ODE framework which avoids the numerical instability related problems noted above, and provides unconditionally accurate gradients. ANODE has a memory footprint of O(L) + O(N_t), with the same computational cost as reversing ODE solve. We furthermore, discuss a memory efficient algorithm which can further reduce this footprint with a trade-off of additional computational cost. We show results on Cifar-10/100 datasets using ResNet and SqueezeNext neural networks.

Connected through hyperedge HE-1 (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"
    - "anodev2 a coupled neural ode evolution framework"
    - "leanconvnets low cost yet effective convolutional neural networks"
    - "regularization for deep learning a taxonomy"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"

Connected through hyperedge HE-2 (induced by source paper "interpolated adjoint method for neural odes"):
  with members:
    - "anodev2 a coupled neural ode evolution framework"
    - "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-3 (induced by source paper "approximation capabilities of neural odes and invertible residual networks"):
  with members:
    - "anodev2 a coupled neural ode evolution framework"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "graph neural ordinary differential equations"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "latent odes for irregularly sampled time series"
    - "graph neural networks exponentially lose expressive power for node classification"
    - "hamiltonian graph networks with ode integrators"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "fast and deep graph neural networks"
    - "a differentiable programming system to bridge machine learning and scientific computing"

Connected through hyperedge HE-5 (induced by source paper "neural odes for image segmentation with level sets"):
  with members:
    - "infocnf an efficient conditional continuous normalizing flow with adaptive solvers"
    - "waveglow a flow based generative network for speech synthesis"
    - "group normalization"
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
    - "collaborative annotation of semantic objects in images with multi granularity supervisions"
    - "neural ordinary differential equations"
    - "squeeze and excitation networks"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "neupde neural network based ordinary and partial differential equations for modeling time dependent data"):
  with members:
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "forward stability of resnet and its variants"
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "machine learning of linear differential equations using gaussian processes"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"

Connected through hyperedge HE-7 (induced by source paper "accelerating neural odes with spectral elements"):
  with members:
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "physics guided recurrent neural networks for modeling dynamical systems application to monitoring water temperature and quality in lakes"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "nais net stable deep networks from non autonomous differential equations"
    - "lstm a search space odyssey"
    - "adaptive computation time for recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "snode spectral discretization of neural odes for system identification"):
  with members:
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "physics guided recurrent neural networks for modeling dynamical systems application to monitoring water temperature and quality in lakes"
    - "stable architectures for deep neural networks"
    - "nais net stable deep networks from non autonomous differential equations"
    - "lstm a search space odyssey"
    - "recurrent highway networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "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)