human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "reservoir computing approaches for representation and classification of multivariate time series"):
  Abstract: Classification of multivariate time series (MTS) has been tackled with a large variety of methodologies and applied to a wide range of scenarios. Among the existing approaches, reservoir computing (RC) techniques, which implement a fixed and high-dimensional recurrent network to process sequential data, are computationally efficient tools to generate a vectorial, fixed-size representation of the MTS, which can be further processed by standard classifiers. Building upon previous works, in this paper we describe and compare several advanced RC-based approaches to generate unsupervised MTS representations, with a specific focus on their capability of yielding an accurate classification. Our main contribution is a new method to encode the MTS within the parameters of a linear model, trained to predict a low-dimensional embedding of the reservoir dynamics. We also study the combination of this representation technique when enhanced with a more complex bidirectional reservoir and non-linear readouts, such as deep neural networks with both fixed and flexible activation functions. We compare with state-of-the-art recurrent networks, standard RC approaches and time series kernels on multiple classification tasks, showing that the proposed algorithms can achieve superior classification accuracy, while being vastly more efficient to train.

Includes members:
  Member "an overview and comparative analysis of recurrent neural networks for short term load forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "forecasting industrial aging processes with machine learning methods")
  Member "bidirectional deep readout echo state networks":
    also connected through other hyperedges:
      (none)
  Member "training echo state networks with regularization through dimensionality reduction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interpreting recurrent neural networks behaviour via excitable network attractors")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "recurrent neural network regularization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "learning phrase representations using rnn encoder decoder for statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "how to construct deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "temporally folded convolutional neural networks for sequence forecasting")
  Member "speech recognition with deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")

Question: Please tell me which class the source paper 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)