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: "ana at semeval 2019 task 3 contextual emotion detection in conversations through hierarchical lstms and bert"
  Abstract: This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchical LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational context. The results show that, in this task, our HRCLE outperforms the most recent state-of-the-art text classification framework: BERT. We combine the results generated by BERT and HRCLE to achieve an overall score of 0.7709 which ranked 5th on the final leader board of the competition among 165 Teams.

Connected through hyperedge HE-1 (induced by source paper "bertscore evaluating text generation with bert"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "fine tune bert for extractive summarization"
    - "alternative weighting schemes for elmo embeddings"
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "pay less attention with lightweight and dynamic convolutions"
    - "simple applications of bert for ad hoc document retrieval"
    - "unifying human and statistical evaluation for natural language generation"
    - "scibert a pretrained language model for scientific text"

Connected through hyperedge HE-2 (induced by source paper "time to take emoji seriously they vastly improve casual conversational models"):
  with members:
    - "a repository of conversational datasets"
    - "training millions of personalized dialogue agents"
    - "a large scale corpus for conversation disentanglement"
    - "coqa a conversational question answering challenge"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "self representation on twitter using emoji skin color modifiers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "modeling multi turn conversation with deep utterance aggregation"

Connected through hyperedge HE-3 (induced by source paper "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"):
  with members:
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "emotionlines an emotion corpus of multi party conversations"
    - "decision support with text based emotion recognition deep learning for affective computing"
    - "memory fusion network for multi view sequential learning"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "neural message passing for quantum chemistry"
    - "quasi recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "emotion recognition in conversation research challenges datasets and recent advances"):
  with members:
    - "snu_ids at semeval 2019 task 3 addressing training test class distribution mismatch in conversational classification"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "emotionlines an emotion corpus of multi party conversations"
    - "deep contextualized word representations"
    - "decision support with text based emotion recognition deep learning for affective computing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

Connected through hyperedge HE-5 (induced by source paper "seq2emo for multi label emotion classification based on latent variable chains transformation"):
  with members:
    - "sgm sequence generation model for multi label classification"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "ntua slp at semeval 2018 task 1 predicting affective content in tweets with deep attentive rnns and transfer learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fasttext zip compressing text classification models"
    - "bidirectional lstm crf models for sequence tagging"
    - "scheduled sampling for sequence prediction with 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)