human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a way out of the odyssey analyzing and combining recent insights for lstms"
  Abstract: LSTMs have become a basic building block for many deep NLP models. In recent years, many improvements and variations have been proposed for deep sequence models in general, and LSTMs in particular. We propose and analyze a series of architectural modifications for LSTM networks resulting in improved performance for text classification datasets. We observe compounding improvements on traditional LSTMs using Monte Carlo test-time model averaging, deep vector averaging (DVA), and residual connections, along with four other suggested modifications. Our analysis provides a simple, reliable, and high quality baseline model.

Connected through hyperedge HE-1 (induced by source paper "recurrently controlled recurrent networks"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "co stack residual affinity networks with multi level attention refinement for matching text sequences"
    - "densely connected bidirectional lstm with applications to sentence classification"
    - "deep contextualized word representations"
    - "multi range reasoning for machine comprehension"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "learning to generate reviews and discovering sentiment"
    - "sentence state lstm for text representation"

Connected through hyperedge HE-2 (induced by source paper "emorl continuous acoustic emotion classification using deep reinforcement learning"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "learning to skim text"
    - "learning to translate in real time with neural machine translation"
    - "exploring the limits of language modeling"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "reinforcement learning neural turing machines revised"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "skip rnn learning to skip state updates in recurrent neural networks"):
  with members:
    - "simple recurrent units for highly parallelizable recurrence"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "training a subsampling mechanism in expectation"
    - "encouraging lstms to anticipate actions very early"
    - "learning to skim text"
    - "phased lstm accelerating recurrent network training for long or event based sequences"

Connected through hyperedge HE-4 (induced by source paper "quasi recurrent neural networks"):
  with members:
    - "sequence to sequence learning as beam search optimization"
    - "densely connected convolutional networks"
    - "neural machine translation in linear time"
    - "strongly typed recurrent neural networks"
    - "dynamic memory networks for visual and textual question answering"
    - "fully character level neural machine translation without explicit segmentation"
    - "pointer sentinel mixture models"
    - "pixel recurrent neural networks"

Question: Please tell me which class the center node 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.CL(Computation and Language)