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: "amobee at iest 2018 transfer learning from language models"
  Abstract: This paper describes the system developed at Amobee for the WASSA 2018 implicit emotions shared task (IEST). The goal of this task was to predict the emotion expressed by missing words in tweets without an explicit mention of those words. We developed an ensemble system consisting of language models together with LSTM-based networks containing a CNN attention mechanism. Our approach represents a novel use of language models (specifically trained on a large Twitter dataset) to predict and classify emotions. Our system reached 1st place with a macro $\text{F}_1$ score of 0.7145.

Connected through hyperedge HE-1 (induced by source paper "towards multimodal emotion recognition in german speech events in cars using transfer learning"):
  with members:
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "learning word vectors for 157 languages"
    - "iest wassa 2018 implicit emotions shared task"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "emotion intensities in tweets"
    - "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"
    - "distant supervision for emotion detection using facebook reactions"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-2 (induced by source paper "latent universal task specific bert"):
  with members:
    - "the evolved transformer"
    - "universal transformers"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "adaptive computation time for recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "amobee at semeval 2019 tasks 5 and 6 multiple choice cnn over contextual embedding"):
  with members:
    - "predicting the type and target of offensive posts in social media"
    - "latent universal task specific bert"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "iest wassa 2018 implicit emotions shared task"):
  with members:
    - "amobee at semeval 2018 task 1 gru neural network with a cnn attention mechanism for sentiment classification"
    - "iiidyt at iest 2018 implicit emotion classification with deep contextualized word representations"
    - "wassa 2017 shared task on emotion intensity"
    - "stance and sentiment in tweets"

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)