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 "exploring interpretable lstm neural networks over multi variable data"):
  Abstract: For recurrent neural networks trained on time series with target and exogenous variables, in addition to accurate prediction, it is also desired to provide interpretable insights into the data. In this paper, we explore the structure of LSTM recurrent neural networks to learn variable-wise hidden states, with the aim to capture different dynamics in multi-variable time series and distinguish the contribution of variables to the prediction. With these variable-wise hidden states, a mixture attention mechanism is proposed to model the generative process of the target. Then we develop associated training methods to jointly learn network parameters, variable and temporal importance w.r.t the prediction of the target variable. Extensive experiments on real datasets demonstrate enhanced prediction performance by capturing the dynamics of different variables. Meanwhile, we evaluate the interpretation results both qualitatively and quantitatively. It exhibits the prospect as an end-to-end framework for both forecasting and knowledge extraction over multi-variable data.

Includes members:
  Member "fine grained attention mechanism for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "signed distance based deep memory recommender")
  Member "an interpretable lstm neural network for autoregressive exogenous model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "what went wrong and when instance wise feature importance for time series models")
  Member "exact and consistent interpretation for piecewise linear neural networks a closed form solution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analytic marching an analytic meshing solution from deep implicit surface networks")
  Member "raim recurrent attentive and intensive model of multimodal patient monitoring data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "what went wrong and when instance wise feature importance for time series models")
  Member "deep multi output forecasting learning to accurately predict blood glucose trajectories":
    also connected through other hyperedges:
      (none)
  Member "methods for interpreting and understanding deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters")
  Member "multilevel wavelet decomposition network for interpretable time series analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for time series classification a review")
  Member "beyond word importance contextual decomposition to extract interactions from lstms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "understanding image captioning models beyond visualizing attention")

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)