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: "how polarized have we become a multimodal classification of trump followers and clinton followers"
  Abstract: Polarization in American politics has been extensively documented and analyzed for decades, and the phenomenon became all the more apparent during the 2016 presidential election, where Trump and Clinton depicted two radically different pictures of America. Inspired by this gaping polarization and the extensive utilization of Twitter during the 2016 presidential campaign, in this paper we take the first step in measuring polarization in social media and we attempt to predict individuals’ Twitter following behavior through analyzing ones’ everyday tweets, profile images and posted pictures. As such, we treat polarization as a classification problem and study to what extent Trump followers and Clinton followers on Twitter can be distinguished, which in turn serves as a metric of polarization in general. We apply LSTM to processing tweet features and we extract visual features using the VGG neural network. Integrating these two sets of features boosts the overall performance. We are able to achieve an accuracy of 69%, suggesting that the high degree of polarization recorded in the literature has started to manifest itself in social media as well.

Connected through hyperedge HE-1 (induced by source paper "fairface face attribute dataset for balanced race gender and age"):
  with members:
    - "diversity in faces"
    - "understanding the political ideology of legislators from social media images"
    - "persuasive faces generating faces in advertisements"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "mitigating unwanted biases with adversarial learning"
    - "learning tasks for multitask learning heterogenous patient populations in the icu"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "single training dimension selection for word embedding with pca"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the dimensionality of word embedding"
    - "nationality classification using name embeddings"
    - "simple and effective dimensionality reduction for word embeddings"
    - "all but the top simple and effective postprocessing for word representations"
    - "compressing word embeddings via deep compositional code learning"
    - "poincar e embeddings for learning hierarchical representations"
    - "swivel improving embeddings by noticing what s missing"

Connected through hyperedge HE-3 (induced by source paper "understanding the political ideology of legislators from social media images"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "image as data automated visual content analysis for political science"
    - "age progression regression by conditional adversarial autoencoder"
    - "protest activity detection and perceived violence estimation from social media images"
    - "from facial expression recognition to interpersonal relation prediction"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "analyzing dynamic ideological communities in congressional voting networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "signed graph analysis for the interpretation of voting behavior"
    - "trump vs hillary what went viral during the 2016 us presidential election"
    - "cohesion and coalition formation in the european parliament roll call votes and twitter activities"
    - "brazilian congress structural balance analysis"
    - "how many political parties should brazil have a data driven method to assess and reduce fragmentation in multi party political systems"

Connected through hyperedge HE-5 (induced by source paper "inference of personal attributes from tweets using machine learning"):
  with members:
    - "quantifying moral foundations from various topics on twitter conversations"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

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)