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: "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
  Abstract: The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementation of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. In this paper we perform a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. We test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. We provide a general overview of the most important architectures and we define guidelines for configuring the recurrent networks to predict real-valued time series.

Connected through hyperedge HE-1 (induced by source paper "forecasting industrial aging processes with machine learning methods"):
  with members:
    - "the autofeat python library for automatic feature engineering and selection"
    - "transfer learning for time series classification"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "pixel recurrent neural networks"
    - "deep learning in neural networks"
    - "advances in optimizing recurrent networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep learning for time series forecasting the electric load case"):
  with members:
    - "deep reinforcement learning for sequence to sequence models"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"
    - "comparative study of cnn and rnn for natural language processing"
    - "pixel recursive super resolution"
    - "convolutional sequence to sequence learning"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-3 (induced by source paper "recurrent neural networks for time series forecasting current status and future directions"):
  with members:
    - "n beats neural basis expansion analysis for interpretable time series forecasting"
    - "sales demand forecast in e commerce using a long short term memory neural network methodology"
    - "gluonts probabilistic time series models in python"
    - "temporal pattern attention for multivariate time series forecasting"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "forecasting across time series databases using recurrent neural networks on groups of similar series a clustering approach"

Connected through hyperedge HE-4 (induced by source paper "analyzing cyber physical systems from the perspective of artificial intelligence"):
  with members:
    - "adversarial resilience learning towards systemic vulnerability analysis for large and complex systems"
    - "gray box adversarial testing for control systems with machine learning component"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "co simulation state of the art"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "practical black box attacks against machine learning"
    - "towards verified artificial intelligence"

Connected through hyperedge HE-5 (induced by source paper "bullwhip effect attenuation in supply chain management via control theoretic tools and short term forecasts a preliminary study with an application to perishable inventories"):
  with members:
    - "la production de nitrites lors de la denitrification des eaux usees par biofiltration strategie de controle et de reduction des concentrations residuelles"
    - "energy saving for building heating via a simple and efficient model free control design first steps with computer simulations"
    - "an efficient model free setting for longitudinal and lateral vehicle control validation through the interconnected pro sivic rtmaps prototyping platform"
    - "non linear estimation is easy"

Connected through hyperedge HE-6 (induced by source paper "recurrent transform learning"):
  with members:
    - "building energy load forecasting using deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning representations for multivariate time series with missing data using temporal kernelized autoencoders"):
  with members:
    - "making the dynamic time warping distance warping invariant"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "structured attention networks"
    - "mida multiple imputation using denoising autoencoders"
    - "multiplex visibility graphs to investigate recurrent neural network dynamics"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"

Connected through hyperedge HE-8 (induced by source paper "general value function networks"):
  with members:
    - "online off policy prediction"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "learning to filter with predictive state inference machines"
    - "temporal difference networks"
    - "learning to predict independent of span"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "temporal difference networks for dynamical systems with continuous observations and actions"
    - "predictive linear gaussian models of stochastic dynamical systems"

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)