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: "emotionx dlc self attentive bilstm for detecting sequential emotions in dialogue"
  Abstract: In this paper, we propose a self-attentive bidirectional long short-term memory (SA-BiLSTM) network to predict multiple emotions for the EmotionX challenge. The BiLSTM exhibits the power of modeling the word dependencies, and extracting the most relevant features for emotion classification. Building on top of BiLSTM, the self-attentive network can model the contextual dependencies between utterances which are helpful for classifying the ambiguous emotions. We achieve 59.6 and 55.0 unweighted accuracy scores in the \textit{Friends} and the \textit{EmotionPush} test sets, respectively.

Connected through hyperedge HE-1 (induced by source paper "hierarchical transformer network for utterance level emotion recognition"):
  with members:
    - "pt code pre trained context dependent encoder for utterance level emotion recognition"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"
    - "knowledge enriched transformer for emotion detection in textual conversations"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "a deep neural framework for contextual affect detection"):
  with members:
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "emotionlines an emotion corpus of multi party conversations"
    - "deep contextualized word representations"
    - "attention is all you need"
    - "recurrent neural network for text classification with multi task learning"
    - "gated feedback recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)