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 "deep reinforcement learning for time series playing idealized trading games"):
  Abstract: Deep Q-learning is investigated as an end-to-end solution to estimate the optimal strategies for acting on time series input. Experiments are conducted on two idealized trading games. 1) Univariate: the only input is a wave-like price time series, and 2) Bivariate: the input includes a random stepwise price time series and a noisy signal time series, which is positively correlated with future price changes. The Univariate game tests whether the agent can capture the underlying dynamics, and the Bivariate game tests whether the agent can utilize the hidden relation among the inputs. Stacked Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) units, Convolutional Neural Network (CNN), and multi-layer perceptron (MLP) are used to model Q values. For both games, all agents successfully find a profitable strategy. The GRU-based agents show best overall performance in the Univariate game, while the MLP-based agents outperform others in the Bivariate game.

Includes members:
  Member "comparative study of cnn and rnn for natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "time series classification from scratch with deep neural networks a strong baseline":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "socialgrid a tcn enhanced method for online discussion forecasting")
  Member "multi scale convolutional neural networks for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "socialgrid a tcn enhanced method for online discussion forecasting")
  Member "hierarchical multiscale recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "gated feedback recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory")
  Member "visualizing and understanding recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "visual summary of value level feature attribution in prediction classes with recurrent neural networks")
  Member "learning to diagnose with lstm recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tensor graph convolutional networks for text classification")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)