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: "riemannian metrics for neural networks ii recurrent networks and learning symbolic data sequences"
  Abstract: Recurrent neural networks are powerful models for sequential data, able to represent complex dependencies in the sequence that simpler models such as hidden Markov models cannot handle. Yet they are notoriously hard to train. Here we introduce a training procedure using a gradient ascent in a Riemannian metric: this produces an algorithm independent from design choices such as the encoding of parameters and unit activities. This metric gradient ascent is designed to have an algorithmic cost close to backpropagation through time for sparsely connected networks. We use this procedure on gated leaky neural networks (GLNNs), a variant of recurrent neural networks with an architecture inspired by finite automata and an evolution equation inspired by continuous-time networks. GLNNs trained with a Riemannian gradient are demonstrated to effectively capture a variety of structures in synthetic problems: basic block nesting as in context-free grammars (an important feature of natural languages, but difficult to learn), intersections of multiple independent Markov-type relations, or long-distance relationships such as the distant-XOR problem. This method does not require adjusting the network structure or initial parameters: the network used is a sparse random graph and the initialization is identical for all problems considered.

Connected through hyperedge HE-1 (induced by source paper "implicit regularization in deep learning"):
  with members:
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "spectrally normalized margin bounds for neural networks"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "deep learning in neural networks"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "path normalized optimization of recurrent neural networks with relu activations"):
  with members:
    - "architectural complexity measures of recurrent neural networks"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "unitary evolution recurrent neural networks"
    - "regularizing rnns by stabilizing activations"
    - "adam a method for stochastic optimization"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

Connected through hyperedge HE-3 (induced by source paper "symmetry invariant optimization in deep networks"):
  with members:
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "path sgd path normalized optimization in deep neural networks"
    - "understanding symmetries in deep networks"
    - "riemannian metrics for neural networks i feedforward networks"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "revisiting natural gradient for deep networks"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-4 (induced by source paper "understanding symmetries in deep networks"):
  with members:
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "path sgd path normalized optimization in deep neural networks"
    - "riemannian metrics for neural networks i feedforward networks"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "revisiting natural gradient for deep networks"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-5 (induced by source paper "training recurrent networks online without backtracking"):
  with members:
    - "riemannian metrics for neural networks i feedforward networks"
    - "on prediction using variable order markov models"

Connected through hyperedge HE-6 (induced by source paper "riemannian metrics for neural networks i feedforward networks"):
  with members:
    - "revisiting natural gradient for deep networks"
    - "efficient riemannian training of recurrent neural networks for learning symbolic data sequences"

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)