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: "a question answering approach to emotion cause extraction"
  Abstract: Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01% in F-measure.

Connected through hyperedge HE-1 (induced by source paper "ecsp a new task for emotion cause span pair extraction and classification"):
  with members:
    - "emotion cause pair extraction a new task to emotion analysis in texts"
    - "rthn a rnn transformer hierarchical network for emotion cause extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural coreference resolution"
    - "attention is all you need"
    - "a minimal span based neural constituency parser"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "end to end emotion cause pair extraction via learning to link"):
  with members:
    - "syntax aware aspect level sentiment classification with proximity weighted convolution network"
    - "deep biaffine attention for neural dependency parsing"
    - "end to end memory networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "rthn a rnn transformer hierarchical network for emotion cause extraction"):
  with members:
    - "from independent prediction to re ordered prediction integrating relative position and global label information to emotion cause identification"
    - "attention is all you need"
    - "detecting concept level emotion cause in microblogging"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "emotion cause pair extraction a new task to emotion analysis in texts"):
  with members:
    - "detecting concept level emotion cause in microblogging"
    - "neural machine translation by jointly learning to align and translate"
    - "speech recognition with deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "from independent prediction to re ordered prediction integrating relative position and global label information to emotion cause identification"):
  with members:
    - "detecting concept level emotion cause in microblogging"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "modeling naive psychology of characters in simple commonsense stories"):
  with members:
    - "controlling linguistic style aspects in neural language generation"
    - "affect lm a neural language model for customizable affective text generation"
    - "personabank a corpus of personal narratives and their story intention graphs"
    - "simulating action dynamics with neural process networks"
    - "dynamic entity representations in neural language models"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "tracking the world state with recurrent entity networks"
    - "neural net models for open domain discourse coherence"

Connected through hyperedge HE-7 (induced by source paper "event2mind commonsense inference on events intents and reactions"):
  with members:
    - "analyzing gender stereotyping in bollywood movies"
    - "detecting and explaining causes from text for a time series event"
    - "learning to paraphrase for question answering"
    - "ordinal common sense inference"
    - "generating sentences from a continuous space"
    - "a large annotated corpus for learning natural language inference"
    - "connotation frames a data driven investigation"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-8 (induced by source paper "incorporating glosses into neural word sense disambiguation"):
  with members:
    - "flexible and creative chinese poetry generation using neural memory"
    - "word sense disambiguation using a bidirectional lstm"
    - "dynamic memory networks for visual and textual question answering"
    - "end to end memory networks"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "adam a method for stochastic optimization"

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)