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 language independent and compositional model for personality trait recognition from short texts"
  Abstract: Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-the-art performance across five traits and three languages (English, Spanish and Italian) compared with prior work in author profiling. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.

Connected through hyperedge HE-1 (induced by source paper "a large scale in depth analysis of developers personalities in the apache ecosystem"):
  with members:
    - "establishing personal trust based connections in distributed teams"
    - "on developers personality in large scale distributed projects the case of the apache ecosystem"
    - "sentiment polarity detection for software development"
    - "a gold standard for emotion annotation in stack overflow"
    - "natural language or not nlon a package for software engineering text analysis pipeline"
    - "emotxt a toolkit for emotion recognition from text"
    - "a preliminary analysis on the effects of propensity to trust in distributed software development"
    - "25 tweets to know you a new model to predict personality with social media"

Connected through hyperedge HE-2 (induced by source paper "learning neural emotion analysis from 100 observations the surprising effectiveness of pre trained word representations"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "learning word vectors for 157 languages"
    - "aff2vec affect enriched distributional word representations"
    - "attention is all you need"
    - "wassa 2017 shared task on emotion intensity"
    - "advances in pre training distributed word representations"
    - "deep multitask learning for semantic dependency parsing"
    - "emotion intensities in tweets"

Connected through hyperedge HE-3 (induced by source paper "globaltrait personality alignment of multilingual word embeddings"):
  with members:
    - "word translation without parallel data"
    - "towards empathetic human robot interactions"
    - "enriching word vectors with subword information"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"

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)