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 "multi variable lstm neural network for autoregressive exogenous model"):
  Abstract: In this paper, we propose multi-variable LSTM capable of accurate forecasting and variable importance interpretation for time series with exogenous variables. Current attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To this end, the multi-variable LSTM equipped with tensorized hidden states is developed to learn hidden states for individual variables, which give rise to our mixture temporal and variable attention. Based on such attention mechanism, we infer and quantify variable importance. Extensive experiments using real datasets with Granger-causality test and the synthetic dataset with ground truth demonstrate the prediction performance and interpretability of multi-variable LSTM in comparison to a variety of baselines. It exhibits the prospect of multi-variable LSTM as an end-to-end framework for both forecasting and knowledge discovery.

Includes members:
  Member "a dual stage attention based recurrent neural network for time series prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "conditional mutual information based contrastive loss for financial time series forecasting")
  Member "learning deep matrix representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical conditional relation networks for video question answering")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "analyzing and exploiting narx recurrent neural networks for long term dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning longer term dependencies via grouped distributor unit")
  Member "factorization tricks for lstm networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressing language models using doped kronecker products")
  Member "modeling long and short term temporal patterns with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks")
  Member "retain interpretable predictive model in healthcare using reverse time attention mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bias in data driven ai systems an introductory survey")
  Member "tensorizing neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse weight activation training")

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.LG(Machine Learning)