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: "emotionlines an emotion corpus of multi party conversations"
  Abstract: Feeling emotion is a critical characteristic to distinguish people from machines. Among all the multi-modal resources for emotion detection, textual datasets are those containing the least additional information in addition to semantics, and hence are adopted widely for testing the developed systems. However, most of the textual emotional datasets consist of emotion labels of only individual words, sentences or documents, which makes it challenging to discuss the contextual flow of emotions. In this paper, we introduce EmotionLines, the first dataset with emotions labeling on all utterances in each dialogue only based on their textual content. Dialogues in EmotionLines are collected from Friends TV scripts and private Facebook messenger dialogues. Then one of seven emotions, six Ekman's basic emotions plus the neutral emotion, is labeled on each utterance by 5 Amazon MTurkers. A total of 29,245 utterances from 2,000 dialogues are labeled in EmotionLines. We also provide several strong baselines for emotion detection models on EmotionLines in this paper.

Connected through hyperedge HE-1 (induced by source paper "eda enriching emotional dialogue acts using an ensemble of neural annotators"):
  with members:
    - "incorporating end to end speech recognition models for sentiment analysis"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "conversational analysis using utterance level attention based bidirectional recurrent neural networks"
    - "discourse wizard discovering deep discourse structure in your conversation with rnns"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "a context based approach for dialogue act recognition using simple recurrent neural networks"
    - "deep contextualized word representations"
    - "neural machine translation in linear time"

Connected through hyperedge HE-2 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "an adversarial approach to high quality sentiment controlled neural dialogue generation"
    - "topic guided variational autoencoders for text generation"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "knowledge aware conversation generation with reasoning on augmented graph"
    - "reinforcement learning based emotional editing constraint conversation generation"
    - "towards an engine for lifelong interactive knowledge learning in human machine conversations"

Connected through hyperedge HE-3 (induced by source paper "bridging dialogue generation and facial expression synthesis"):
  with members:
    - "dynamically context sensitive time decay attention for dialogue modeling"
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "integrating planning for task completion dialogue policy learning"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "investigating linguistic pattern ordering in hierarchical natural language generation"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-4 (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"
    - "ana at semeval 2019 task 3 contextual emotion detection in conversations through hierarchical lstms and bert"
    - "meld a multimodal multi party dataset for emotion recognition in 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-5 (induced by source paper "pt code pre trained context dependent encoder for utterance level emotion recognition"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "on the state of the art of evaluation in neural language models"
    - "emotion detection on tv show transcripts with sequence based convolutional neural networks"
    - "exploring the limits of language modeling"
    - "ask me anything dynamic memory networks for natural language processing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "emotionx ku bert max based contextual emotion classifier"):
  with members:
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "emotion detection on tv show transcripts with sequence based convolutional neural networks"
    - "dialogue act sequence labeling using hierarchical encoder with crf"

Connected through hyperedge HE-7 (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"
    - "ana at semeval 2019 task 3 contextual emotion detection in conversations through hierarchical lstms and bert"
    - "meld a multimodal multi party dataset for emotion recognition in 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-8 (induced by source paper "emotionx hsu adopting pre trained bert for emotion classification"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "attnconvnet at semeval 2018 task 1 attention based convolutional neural networks for multi label emotion classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "recurrent neural network for text classification with multi task learning"
    - "convolutional neural networks for sentence classification"

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)