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 "test metrics for recurrent neural networks"):
  Abstract: Recurrent neural networks (RNNs) have been applied to a broad range of application areas such as natural language processing, drug discovery, and video recognition. This paper develops a coverage-guided test framework, including three test metrics and a mutation-based test case generation method, for the validation of a major class of RNNs, i.e., long short-term memory networks (LSTMs). The test metrics are designed with respect to the internal structures of the LSTM layers to quantify the information of the forget gate, the one-step information change of an aggregate hidden state, and the multi-step information evolution of positive and negative aggregate hidden state, respectively. We apply the test framework to several typical LSTM applications, including a network trained on IMDB movie reviews for sentiment analysis, a network trained on MNIST dataset for image classification, and a network trained on a lipophilicity dataset for scientific machine learning. Our experimental results show that the coverage-guided testing can be used to not only extensively exploit the behaviour of the LSTM layer in order to discover the safety loopholes (such as adversarial examples) but also help understand the internal mechanism of how the LSTM layer processes data.

Includes members:
  Member "eda easy data augmentation techniques for boosting performance on text classification tasks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "snippext semi supervised opinion mining with augmented data")
  Member "recurrent neural networks for time series forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph neural ordinary differential equations")
  Member "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reliability validation of learning enabled vehicle tracking")
  Member "on adversarial examples for character level neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "deepcruiser automated guided testing for stateful deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rnn test adversarial testing framework for recurrent neural network systems")
  Member "symbolic execution for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "importance driven deep learning system testing")
  Member "towards dependability metrics for neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "engineering problems in machine learning systems")
  Member "deepmutation mutation testing of deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "importance driven deep learning system testing")

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)