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: "dialoguernn an attentive rnn for emotion detection in conversations"
  Abstract: Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc. Currently, systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance. In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification. Our model outperforms the state of the art by a significant margin on two different datasets.

Connected through hyperedge HE-1 (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"
    - "knowledge enriched transformer for emotion detection in textual conversations"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "multi task learning with language modeling for question generation"
    - "deep contextualized word representations"
    - "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-2 (induced by source paper "multilogue net a context aware rnn for multi modal emotion detection and sentiment analysis in conversation"):
  with members:
    - "memory fusion network for multi view sequential learning"
    - "gated attention readers for text comprehension"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "knowledge enriched transformer for emotion detection in textual conversations"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "global to local memory pointer networks for task oriented dialogue"
    - "ntuer at semeval 2019 task 3 emotion classification with word and sentence representations in rcnn"
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "transformer xl attentive language models beyond a fixed length context"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "improving the transformer translation model with document level context"

Connected through hyperedge HE-4 (induced by source paper "ntuer at semeval 2019 task 3 emotion classification with word and sentence representations in rcnn"):
  with members:
    - "deep contextualized word representations"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "an affect rich neural conversational model with biased attention and weighted cross entropy loss"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-5 (induced by source paper "towards multimodal sarcasm detection an _obviously_ perfect paper"):
  with members:
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "representing social media users for sarcasm detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "tensor fusion network for multimodal sentiment analysis"
    - "harnessing cognitive features for sarcasm detection"

Connected through hyperedge HE-6 (induced by source paper "recognition of advertisement emotions with application to computational advertising"):
  with members:
    - "affect recognition in ads with application to computational advertising"
    - "learning transferable architectures for scalable image recognition"
    - "deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders"
    - "evaluating content centric vs user centric ad affect recognition"
    - "discovering gender differences in facial emotion recognition via implicit behavioral cues"
    - "object detection can be improved using human derived contextual expectations"
    - "deep feature learning for eeg recordings"
    - "dexpression deep convolutional neural network for expression recognition"

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"
    - "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"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

Connected through hyperedge HE-8 (induced by source paper "phonetic enriched text representation for chinese sentiment analysis with reinforcement learning"):
  with members:
    - "multi attention recurrent network for human communication comprehension"
    - "tensor fusion network for multimodal sentiment analysis"
    - "learning chinese word representations from glyphs of characters"
    - "learning character level compositionality with visual features"
    - "interactive attention networks for aspect level sentiment classification"
    - "component enhanced chinese character embeddings"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"

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)