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: "accelerating neural odes with spectral elements"
  Abstract: This paper proposes the use of spectral element methods \citep{canuto_spectral_1988} for fast and accurate training of Neural Ordinary Differential Equations (ODE-Nets; \citealp{Chen2018NeuralOD}). This is achieved by expressing their dynamics as truncated series of Legendre polynomials. The series coefficients, as well as the network weights, are computed by minimizing the weighted sum of the loss function and the violation of the ODE-Net dynamics. The problem is solved by coordinate descent that alternately minimizes, with respect to the coefficients and the weights, two unconstrained sub-problems using standard backpropagation and gradient methods. The resulting optimization scheme is fully time-parallel and results in a low memory footprint. Experimental comparison to standard methods, such as backpropagation through explicit solvers and the adjoint technique \citep{Chen2018NeuralOD}, on training surrogate models of small and medium-scale dynamical systems shows that it is at least one order of magnitude faster at reaching a comparable value of the loss function. The corresponding testing MSE is one order of magnitude smaller as well, suggesting generalization capabilities increase.

Connected through hyperedge HE-1 (induced by source paper "neural lyapunov model predictive control"):
  with members:
    - "deep value model predictive control"
    - "differentiable convex optimization layers"
    - "tustin neural networks a class of recurrent nets for adaptive mpc of mechanical systems"
    - "when to trust your model model based policy optimization"
    - "pytorch an imperative style high performance deep learning library"
    - "differentiable mpc for end to end planning and control"
    - "lipschitz continuity in model based reinforcement learning"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-2 (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"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "hamiltonian graph networks with ode integrators"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "fast and deep graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "on robustness of neural ordinary differential equations"):
  with members:
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"
    - "analyzing inverse problems with invertible neural networks"
    - "feature denoising for improving adversarial robustness"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "deep neural nets with interpolating function as output activation"
    - "adversarial defense via data dependent activation function and total variation minimization"
    - "neural ordinary differential equations"

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.NE(Neural and Evolutionary Computing)