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: "emotion representation mapping for automatic lexicon construction mostly performs on human level"
  Abstract: Emotion Representation Mapping (ERM) has the goal to convert existing emotion ratings from one representation format into another one, e.g., mapping Valence-Arousal-Dominance annotations for words or sentences into Ekman's Basic Emotions and vice versa. ERM can thus not only be considered as an alternative to Word Emotion Induction (WEI) techniques for automatic emotion lexicon construction but may also help mitigate problems that come from the proliferation of emotion representation formats in recent years. We propose a new neural network approach to ERM that not only outperforms the previous state-of-the-art. Equally important, we present a refined evaluation methodology and gather strong evidence that our model yields results which are (almost) as reliable as human annotations, even in cross-lingual settings. Based on these results we generate new emotion ratings for 13 typologically diverse languages and claim that they have near-gold quality, at least.

Connected through hyperedge HE-1 (induced by source paper "joint emotion label space modelling for affect lexica"):
  with members:
    - "combining sentiment lexica with a multi view variational autoencoder"
    - "learning neural emotion analysis from 100 observations the surprising effectiveness of pre trained word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "word affect intensities"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-2 (induced by source paper "a time series analysis of emotional loading in central bank statements"):
  with members:
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-3 (induced by source paper "modeling word emotion in historical language quantity beats supposed stability in seed word selection"):
  with members:
    - "representation mapping a novel approach to generate high quality multi lingual emotion lexicons"
    - "deep contextualized word representations"
    - "jeseme a website for exploring diachronic changes in word meaning and emotion"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "factors influencing the surprising instability of word embeddings"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "ultradense word embeddings by orthogonal transformation"
    - "diachronic word embeddings reveal statistical laws of semantic change"

Connected through hyperedge HE-4 (induced by source paper "inducing affective lexical semantics in historical language"):
  with members:
    - "representation mapping a novel approach to generate high quality multi lingual emotion lexicons"
    - "deep contextualized word representations"
    - "jeseme a website for exploring diachronic changes in word meaning and emotion"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "ultradense word embeddings by orthogonal transformation"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "modeling empathy and distress in reaction to news stories"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "wassa 2017 shared task on emotion intensity"
    - "advances in pre training distributed word representations"
    - "emotion intensities in tweets"
    - "enriching word vectors with subword information"
    - "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)