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: "meld a multimodal multi party dataset for emotion recognition in conversations"
  Abstract: Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at http:// this http URL.

Connected through hyperedge HE-1 (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"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "improving the transformer translation model with document level context"
    - "emotion detection in text a review"

Connected through hyperedge HE-2 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey"):
  with members:
    - "pdanet polarity consistent deep attention network for fine grained visual emotion regression"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "multi attention recurrent network for human communication comprehension"
    - "a data driven approach to mid level perceptual musical feature modeling"
    - "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"

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"
    - "ana at semeval 2019 task 3 contextual emotion detection in conversations through hierarchical lstms and bert"
    - "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 "ur funny a multimodal language dataset for understanding humor"):
  with members:
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multimodal language analysis with recurrent multistage fusion"
    - "modality based factorization for multimodal fusion"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "learning factorized multimodal representations"

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"
    - "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"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "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 "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"
    - "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-7 (induced by source paper "real time emotion recognition via attention gated hierarchical memory network"):
  with members:
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "end to end memory networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-8 (induced by source paper "sentiment and sarcasm classification with multitask learning"):
  with members:
    - "multi attention recurrent network for human communication comprehension"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "effective approaches to attention based neural machine translation"
    - "ask me anything dynamic memory networks for natural language processing"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "sequence to sequence learning with neural networks"

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)