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: "nais net stable deep networks from non autonomous differential equations"
  Abstract: This paper introduces "Non-Autonomous Input-Output Stable Network" (NAIS-Net), a very deep architecture where each stacked processing block is derived from a time-invariant non-autonomous dynamical system. Non-autonomy is implemented by skip connections from the block input to each of the unrolled processing stages and allows stability to be enforced so that blocks can be unrolled adaptively to a pattern-dependent processing depth. We prove that the network is globally asymptotically stable so that for every initial condition there is exactly one input-dependent equilibrium assuming tanh units, and multiple stable equilibria for ReLU units. An efficient implementation that enforces the stability under derived conditions for both fully-connected and convolutional layers is also presented. Experimental results show how NAIS-Net exhibits stability in practice, yielding a significant reduction in generalization gap compared to ResNets.

Connected through hyperedge HE-1 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "recent advances in recurrent neural networks"
    - "lstm a search space odyssey"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"

Connected through hyperedge HE-2 (induced by source paper "accelerating neural odes with spectral elements"):
  with members:
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "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"
    - "lstm a search space odyssey"
    - "adaptive computation time for recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "imexnet a forward stable deep neural network"):
  with members:
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "model reduction with memory and the machine learning of dynamical systems"
    - "neural ordinary differential equations"
    - "the reversible residual network backpropagation without storing activations"
    - "visualizing the loss landscape of neural nets"
    - "reversible architectures for arbitrarily deep residual neural networks"
    - "learning a variational network for reconstruction of accelerated mri data"

Connected through hyperedge HE-4 (induced by source paper "snode spectral discretization of neural odes for system identification"):
  with members:
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "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"
    - "lstm a search space odyssey"
    - "recurrent highway networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "anode unconditionally accurate memory efficient gradients for neural odes"):
  with members:
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "squeezenext hardware aware neural network design"
    - "the reversible residual network backpropagation without storing activations"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "reversible architectures for arbitrarily deep residual neural networks"
    - "density estimation using real nvp"

Connected through hyperedge HE-6 (induced by source paper "anodev2 a coupled neural ode evolution framework"):
  with members:
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "identity mappings in deep residual networks"
    - "convolution by evolution differentiable pattern producing networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "invertible residual networks"):
  with members:
    - "hybrid models with deep and invertible features"
    - "image transformer"
    - "stochastic chebyshev gradient descent for spectral optimization"
    - "backpropagation for implicit spectral densities"
    - "sorting out lipschitz function approximation"
    - "analyzing inverse problems with invertible neural networks"
    - "the singular values of convolutional layers"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"

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)