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: "unsupervised user stance detection on twitter"
  Abstract: We present a highly effective unsupervised framework for detecting the stance of prolific Twitter users with respect to controversial topics. In particular, we use dimensionality reduction to project users onto a low-dimensional space, followed by clustering, which allows us to find core users that are representative of the different stances. Our framework has three major advantages over pre-existing methods, which are based on supervised or semi-supervised classification. First, we do not require any prior labeling of users: instead, we create clusters, which are much easier to label manually afterwards, e.g., in a matter of seconds or minutes instead of hours. Second, there is no need for domain- or topic-level knowledge either to specify the relevant stances (labels) or to conduct the actual labeling. Third, our framework is robust in the face of data skewness, e.g., when some users or some stances have greater representation in the data. We experiment with different combinations of user similarity features, dataset sizes, dimensionality reduction methods, and clustering algorithms to ascertain the most effective and most computationally efficient combinations across three different datasets (in English and Turkish). We further verified our results on additional tweet sets covering six different controversial topics. Our best combination in terms of effectiveness and efficiency uses retweeted accounts as features, UMAP for dimensionality reduction, and Mean Shift for clustering, and yields a small number of high-quality user clusters, typically just 2--3, with more than 98% purity. The resultant user clusters can be used to train downstream classification. Moreover, our framework is robust to variations in the hyper-parameter values and also with respect to random initialization.

Connected through hyperedge HE-1 (induced by source paper "quantifying polarization on twitter the kavanaugh nomination"):
  with members:
    - "devam vs tamam 2018 turkish elections"
    - "automatic stance detection using end to end memory networks"
    - "trump vs hillary what went viral during the 2016 us presidential election"
    - "bag of tricks for efficient text classification"
    - "failedrevolutions using twitter to study the antecedents of isis support"
    - "content and network dynamics behind egyptian political polarization on twitter"

Connected through hyperedge HE-2 (induced by source paper "your stance is exposed analysing possible factors for stance detection on social media"):
  with members:
    - "assessing sentiment of the expressed stance on social media"
    - "automatic stance detection using end to end memory networks"
    - "topical stance detection for twitter a two phase lstm model using attention"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "hiding individuals and communities in a social network"
    - "friends and enemies of clinton and trump using context for detecting stance in political tweets"
    - "reducing controversy by connecting opposing views"
    - "stance detection with bidirectional conditional encoding"

Connected through hyperedge HE-3 (induced by source paper "embedding based qualitative analysis of polarization in turkey"):
  with members:
    - "devam vs tamam 2018 turkish elections"
    - "community interaction and conflict on the web"
    - "to kavanaugh or not to kavanaugh that is the polarizing question"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "semantics derived automatically from language corpora contain human like biases"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "failedrevolutions using twitter to study the antecedents of isis support"

Connected through hyperedge HE-4 (induced by source paper "tanbih get to know what you are reading"):
  with members:
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "multilingual clustering of streaming news"
    - "predicting factuality of reporting and bias of news media sources"
    - "neural discourse structure for text categorization"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-5 (induced by source paper "predicting the topical stance of media and popular twitter users"):
  with members:
    - "to kavanaugh or not to kavanaugh that is the polarizing question"
    - "bag of tricks for efficient text classification"
    - "failedrevolutions using twitter to study the antecedents of isis support"
    - "content and network dynamics behind egyptian political polarization on twitter"

Connected through hyperedge HE-6 (induced by source paper "assessing sentiment of the expressed stance on social media"):
  with members:
    - "political discussions in homogeneous and cross cutting communication spaces"
    - "stance and sentiment in tweets"

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)