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: "25 tweets to know you a new model to predict personality with social media"
  Abstract: Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users written text require too much input data to be realistically used in the context of social media. In this work, we aim to drastically reduce the data requirement for personality modeling and develop a model that is applicable to most users on Twitter. Our model integrates Word Embedding features with Gaussian Processes regression. Based on the evaluation of over 1.3K users on Twitter, we find that our model achieves comparable or better accuracy than state of the art techniques with 8 times fewer data.

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"
    - "links between the personalities styles and performance in computer programming"

Connected through hyperedge HE-2 (induced by source paper "automatic extraction of personality from text challenges and opportunities"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-3 (induced by source paper "couplenet paying attention to couples with coupled attention for relationship recommendation"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "multi pointer co attention networks for recommendation"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "cross temporal recurrent networks for ranking question answer pairs"
    - "effective approaches to attention based neural machine translation"

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.SI(Social and Information Networks)