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 "financial series prediction using attention lstm"):
  Abstract: Financial time series prediction, especially with machine learning techniques, is an extensive field of study. In recent times, deep learning methods (especially time series analysis) have performed outstandingly for various industrial problems, with better prediction than machine learning methods. Moreover, many researchers have used deep learning methods to predict financial time series with various models in recent years. In this paper, we will compare various deep learning models, such as multilayer perceptron (MLP), one-dimensional convolutional neural networks (1D CNN), stacked long short-term memory (stacked LSTM), attention networks, and weighted attention networks for financial time series prediction. In particular, attention LSTM is not only used for prediction, but also for visualizing intermediate outputs to analyze the reason of prediction; therefore, we will show an example for understanding the model prediction intuitively with attention vectors. In addition, we focus on time and factors, which lead to an easy understanding of why certain trends are predicted when accessing a given time series table. We also modify the loss functions of the attention models with weighted categorical cross entropy; our proposed model produces a 0.76 hit ratio, which is superior to those of other methods for predicting the trends of the KOSPI 200.

Includes members:
  Member "qanet combining local convolution with global self attention for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on feature normalization and data augmentation")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "an artificial neural network based stock trading system using technical analysis and big data framework":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "variational quantum circuits and deep reinforcement learning")
  Member "financial series prediction comparison between precision of time series models and machine learning methods":
    also connected through other hyperedges:
      (none)
  Member "deep sentence embedding using long short term memory networks analysis and application to information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "convolutional neural networks for sentence classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "neural machine translation by jointly learning to align and translate":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "show and tell a neural image caption generator":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

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)