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: "knowledge enriched transformer for emotion detection in textual conversations"
  Abstract: Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions. In this paper, we address these challenges by proposing a Knowledge-Enriched Transformer (KET), where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mechanism. Experiments on multiple textual conversation datasets demonstrate that both context and commonsense knowledge are consistently beneficial to the emotion detection performance. In addition, the experimental results show that our KET model outperforms the state-of-the-art models on most of the tested datasets in F1 score.

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"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "multi task learning with auxiliary speaker identification for conversational emotion recognition"):
  with members:
    - "multi task networks with universe group and task feature learning"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "multi task learning with language modeling for question generation"
    - "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "adversarial multi task learning for text classification"

Connected through hyperedge HE-3 (induced by source paper "multi task learning network for emotion recognition in conversation"):
  with members:
    - "multi task networks with universe group and task feature learning"
    - "different absorption from the same sharing sifted multi task learning for fake news detection"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "multi task learning with language modeling for question generation"
    - "deep contextualized word representations"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "emotion detection on tv show transcripts with sequence based convolutional neural networks"
    - "end to end multimodal emotion recognition using deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "emotion recognition in conversations with transfer learning from generative conversation modeling"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "transfer learning for sequence labeling using source model and target data"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"

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)