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: "identifying users with opposing opinions in twitter debates"
  Abstract: In recent times, social media sites such as Twitter have been extensively used for debating politics and public policies. These debates span millions of tweets and numerous topics of public importance. Thus, it is imperative that this vast trove of data is tapped in order to gain insights into public opinion especially on hotly contested issues such as abortion, gun reforms etc. Thus, in our work, we aim to gauge users' stance on such topics in Twitter. We propose ReLP, a semi-supervised framework using a retweet-based label propagation algorithm coupled with a supervised classifier to identify users with differing opinions. In particular, our framework is designed such that it can be easily adopted to different domains with little human supervision while still producing excellent accuracy

Connected through hyperedge HE-1 (induced by source paper "vocabulary based method for quantifying controversy in social media"):
  with members:
    - "quantifying polarization on twitter the kavanaugh nomination"
    - "community interaction and conflict on the web"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "a distance measure for the analysis of polar opinion dynamics in social networks"
    - "characterizing information diets of social media users"
    - "using word embeddings in twitter election classification"
    - "reducing controversy by connecting opposing views"

Connected through hyperedge HE-2 (induced by source paper "your stance is exposed analysing possible factors for stance detection on social media"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "characterizing long running political phenomena on social media"):
  with members:
    - "mitre at semeval 2016 task 6 transfer learning for stance detection"
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-4 (induced by source paper "360 deg stance detection"):
  with members:
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "estimating collective judgement of rumours in social media"

Connected through hyperedge HE-5 (induced by source paper "deep memory networks for attitude identification"):
  with members:
    - "aspect level sentiment classification with deep memory network"
    - "stance and sentiment in tweets"
    - "end to end memory networks"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"
    - "automatic aggregation by joint modeling of aspects and values"

Connected through hyperedge HE-6 (induced by source paper "mitre at semeval 2016 task 6 transfer learning for stance detection"):
  with members:
    - "learning distributed representations of sentences from unlabelled data"
    - "skip thought vectors"
    - "semi supervised sequence learning"
    - "how transferable are features in deep neural networks"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "get out the vote determining support or opposition from congressional floor debate transcripts"

Connected through hyperedge HE-7 (induced by source paper "stance and sentiment in tweets"):
  with members:
    - "mitre at semeval 2016 task 6 transfer learning for stance detection"
    - "bridging social media via distant supervision"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "distributed representations of words and phrases and their compositionality"
    - "a survey of paraphrasing and textual entailment methods"
    - "get out the vote determining support or opposition from congressional floor debate transcripts"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"

Connected through hyperedge HE-8 (induced by source paper "stance detection with bidirectional conditional encoding"):
  with members:
    - "emoji2vec learning emoji representations from their description"
    - "mitre at semeval 2016 task 6 transfer learning for stance detection"
    - "reasoning about entailment with neural attention"
    - "a large annotated corpus for learning natural language inference"
    - "adam a method for stochastic optimization"
    - "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)