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: "a primal dual method for training recurrent neural networks constrained by the echo state property"
  Abstract: We present an architecture of a recurrent neural network (RNN) with a fully-connected deep neural network (DNN) as its feature extractor. The RNN is equipped with both causal temporal prediction and non-causal look-ahead, via auto-regression (AR) and moving-average (MA), respectively. The focus of this paper is a primal-dual training method that formulates the learning of the RNN as a formal optimization problem with an inequality constraint that provides a sufficient condition for the stability of the network dynamics. Experimental results demonstrate the effectiveness of this new method, which achieves 18.86% phone recognition error on the TIMIT benchmark for the core test set. The result approaches the best result of 17.7%, which was obtained by using RNN with long short-term memory (LSTM). The results also show that the proposed primal-dual training method produces lower recognition errors than the popular RNN methods developed earlier based on the carefully tuned threshold parameter that heuristically prevents the gradient from exploding.

Connected through hyperedge HE-1 (induced by source paper "deep sentence embedding using long short term memory networks analysis and application to information retrieval"):
  with members:
    - "skip thought vectors"
    - "convolutional neural network architectures for matching natural language sentences"
    - "visualizing and understanding recurrent networks"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "distributed representations of sentences and documents"
    - "multilingual models for compositional distributed semantics"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "deep sentence embedding using the long short term memory networks"):
  with members:
    - "convolutional neural network architectures for matching natural language sentences"
    - "visualizing and understanding recurrent networks"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "distributed representations of sentences and documents"
    - "multilingual models for compositional distributed semantics"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-3 (induced by source paper "how to construct deep recurrent neural networks"):
  with members:
    - "on the number of response regions of deep feed forward networks with piece wise linear activations"
    - "speech recognition with deep recurrent neural networks"
    - "learned norm pooling for deep feedforward and recurrent neural networks"
    - "efficient estimation of word representations in vector space"
    - "revisiting natural gradient for deep networks"
    - "distributed representations of words and phrases and their compositionality"
    - "recurrent convolutional neural networks for scene parsing"
    - "generating sequences with recurrent 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.LG(Machine Learning)