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: "forecasting across time series databases using recurrent neural networks on groups of similar series a clustering approach"
  Abstract: With the advent of Big Data, nowadays in many applications databases containing large quantities of similar time series are available. Forecasting time series in these domains with traditional univariate forecasting procedures leaves great potentials for producing accurate forecasts untapped. Recurrent neural networks (RNNs), and in particular Long Short-Term Memory (LSTM) networks, have proven recently that they are able to outperform state-of-the-art univariate time series forecasting methods in this context when trained across all available time series. However, if the time series database is heterogeneous, accuracy may degenerate, so that on the way towards fully automatic forecasting methods in this space, a notion of similarity between the time series needs to be built into the methods. To this end, we present a prediction model that can be used with different types of RNN models on subgroups of similar time series, which are identified by time series clustering techniques. We assess our proposed methodology using LSTM networks, a widely popular RNN variant. Our method achieves competitive results on benchmarking datasets under competition evaluation procedures. In particular, in terms of mean sMAPE accuracy, it consistently outperforms the baseline LSTM model and outperforms all other methods on the CIF2016 forecasting competition dataset.

Connected through hyperedge HE-1 (induced by source paper "comprehensive analysis of time series forecasting using neural networks"):
  with members:
    - "integration of static and dynamic analysis for malware family classification with composite neural network"
    - "combining static and dynamic features for multivariate sequence classification"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (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"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"

Connected through hyperedge HE-3 (induced by source paper "tsxplain demystification of dnn decisions for time series using natural language and statistical features"):
  with members:
    - "tsviz demystification of deep learning models for time series analysis"
    - "interpretable convolutional neural networks via feedforward design"
    - "textual explanations for self driving vehicles"
    - "neural network interpretation via fine grained textual summarization"
    - "network dissection quantifying interpretability of deep visual representations"
    - "generating visual explanations"
    - "understanding neural networks through deep visualization"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-4 (induced by source paper "recurrent neural networks for time series forecasting"):
  with members:
    - "lstm a search space odyssey"
    - "deep learning for time series analysis"
    - "multi period time series modeling with sparsity via bayesian variational inference"
    - "dropout feature ranking for deep learning models"
    - "recurrent neural networks for multivariate time series with missing values"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-5 (induced by source paper "sales demand forecast in e commerce using a long short term memory neural network methodology"):
  with members:
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "sales forecast in e commerce using convolutional neural network"
    - "training deep networks without learning rates through coin betting"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "draw a recurrent neural network for image generation"
    - "learning to diagnose with lstm recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with 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)