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 "log message anomaly detection and classification using auto b lstm and auto gru"):
  Abstract: Log messages are now widely used in software systems. They are important for classification as millions of logs are generated each day. Most logs are unstructured which makes classification a challenge. In this paper, Deep Learning (DL) methods called Auto-LSTM, Auto-BLSTM and Auto-GRU are developed for anomaly detection and log classification. These models are used to convert unstructured log data to trained features which is suitable for classification algorithms. They are evaluated using four data sets, namely BGL, Openstack, Thunderbird and IMDB. The first three are popular log data sets while the fourth is a movie review data set which is used for sentiment classification and is used here to show that the models can be generalized to other text classification tasks. The results obtained show that Auto-LSTM, Auto-BLSTM and Auto-GRU perform better than other well-known algorithms.

Includes members:
  Member "tools and benchmarks for automated log parsing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self supervised log parsing")
  Member "adversarial variational bayes unifying variational autoencoders and generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial code learning for image generation")
  Member "regularizing deep neural networks by noise its interpretation and optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "text classification improved by integrating bidirectional lstm with two dimensional max pooling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sentence level human translation quality estimation with attention based neural networks")
  Member "long short term memory based recurrent neural network architectures for large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "long term recurrent convolutional networks for visual recognition and description":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "recurrent neural network regularization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "learning phrase representations using rnn encoder decoder for statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformation based adversarial video prediction on large scale data")

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)