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: "training echo state networks with regularization through dimensionality reduction"
  Abstract: In this paper, we introduce a new framework to train a class of recurrent neural network, called Echo State Network, to predict real valued time-series and to provide a visualization of the modeled system dynamics. The method consists in projecting the output of the internal layer of the network on a lower dimensional space, before training the output layer to learn the target task. Notably, we enforce a regularization constraint that leads to better generalization capabilities. We evaluate the performances of our approach on several benchmark tests, using different techniques to train the readout of the network, achieving superior predictive performance when using the proposed framework. Finally, we provide an insight on the effectiveness of the implemented mechanics through a visualization of the trajectory in the phase space and relying on the methodologies of nonlinear time-series analysis. By applying our method on well-known chaotic systems, we provide evidence that the lower dimensional embedding retains the dynamical properties of the underlying system better than the full-dimensional internal states of the network.

Connected through hyperedge HE-1 (induced by source paper "interpreting recurrent neural networks behaviour via excitable network attractors"):
  with members:
    - "full force a target based method for training recurrent networks"
    - "can recurrent neural networks warp time"
    - "methods for interpreting and understanding deep neural networks"
    - "orthogonal echo state networks and stochastic evaluations of likelihoods"
    - "theory guided data science a new paradigm for scientific discovery from data"
    - "an overview of gradient descent optimization algorithms"
    - "unitary evolution recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "reservoir computing approaches for representation and classification of multivariate time series"):
  with members:
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "bidirectional deep readout echo state networks"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "how to construct deep recurrent neural networks"
    - "speech recognition with deep recurrent neural networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "temporal overdrive recurrent neural network"):
  with members:
    - "lstm a search space odyssey"
    - "gated feedback recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "generating sequences with recurrent neural networks"
    - "advances in optimizing recurrent networks"

Connected through hyperedge HE-4 (induced by source paper "bidirectional deep readout echo state networks"):
  with members:
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "sequence to sequence learning with neural networks"
    - "how to construct deep recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "orthogonal echo state networks and stochastic evaluations of likelihoods"):
  with members:
    - "echo state condition at the critical point"
    - "input anticipating critical reservoirs show power law forgetting of unexpected input events"

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)