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: "norm preserving orthogonal permutation linear unit activation functions oplu"
  Abstract: We propose a novel activation function that implements piece-wise orthogonal non-linear mappings based on permutations. It is straightforward to implement, and very computationally efficient, also it has little memory requirements. We tested it on two toy problems for feedforward and recurrent networks, it shows similar performance to tanh and ReLU. OPLU activation function ensures norm preservance of the backpropagated gradients, therefore it is potentially good for the training of deep, extra deep, and recurrent neural networks.

Connected through hyperedge HE-1 (induced by source paper "preventing gradient attenuation in lipschitz constrained convolutional networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "universal lipschitz approximation in bounded depth neural networks"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "provable robustness against all adversarial l_p perturbations for p geq 1"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"
    - "sorting out lipschitz function approximation"
    - "limitations of the lipschitz constant as a defense against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "universal lipschitz approximation in bounded depth neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "scaling provable adversarial defenses"
    - "lipschitz regularized deep neural networks generalize and are adversarially robust"
    - "sorting out lipschitz function approximation"
    - "limitations of the lipschitz constant as a defense against adversarial examples"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "sorting out lipschitz function approximation"):
  with members:
    - "limitations of the lipschitz constant as a defense against adversarial examples"
    - "the singular values of convolutional layers"
    - "aggregated momentum stability through passive damping"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "spectral normalization for generative adversarial networks"
    - "on convergence and stability of gans"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-4 (induced by source paper "on orthogonality and learning recurrent networks with long term dependencies"):
  with members:
    - "gated orthogonal recurrent units on learning to forget"
    - "generalized backpropagation etude de cas orthogonality"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "orthogonal rnns and long memory tasks"
    - "tunable efficient unitary neural networks eunn and their application to rnns"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "unitary evolution recurrent neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-5 (induced by source paper "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"):
  with members:
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "orthogonal rnns and long memory tasks"
    - "unitary evolution recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "a clockwork rnn"

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)