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: "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
  Abstract: Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite the large amount of work addressing this question, there has been no clear validation of online social media opinion trend with traditional surveys. Here we develop a method to infer the opinion of Twitter users by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to build an in-domain training set of the order of a million tweets. We validate our method in the context of 2016 US Presidential Election by comparing the Twitter opinion trend with the New York Times National Polling Average, representing an aggregate of hundreds of independent traditional polls. The Twitter opinion trend follows the aggregated NYT polls with remarkable accuracy. We investigate the dynamics of the social network formed by the interactions among millions of Twitter supporters and infer the support of each user to the presidential candidates. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of traditional surveys.

Connected through hyperedge HE-1 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information"):
  with members:
    - "false news on social media a data driven survey"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "fake news detection on social media using geometric deep learning"
    - "an information theoretic all scales approach to comparing networks"
    - "anatomy of an online misinformation network"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "the spread of low credibility content by social bots"
    - "feature selection a data perspective"

Connected through hyperedge HE-2 (induced by source paper "measuring relative opinion from location based social media a case study of the 2016 u s presidential election"):
  with members:
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "scaling laws in geo located twitter data"
    - "using word embeddings in twitter election classification"
    - "distributed representations of sentences and documents"
    - "deep learning of representations looking forward"
    - "distributed representations of words and phrases and their compositionality"
    - "a meta analysis of state of the art electoral prediction from twitter data"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-3 (induced by source paper "modeling the popularity of twitter hashtags with master equations"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "modeling the infectiousness of twitter hashtags"
    - "virality prediction and community structure in social networks"
    - "the pulse of news in social media forecasting popularity"
    - "stochastic models of user contributory web sites"
    - "novelty and collective attention"

Connected through hyperedge HE-4 (induced by source paper "text mining forma mentis networks reconstruct public perception of the stem gender gap in social media"):
  with members:
    - "from topic networks to distributed cognitive maps zipfian topic universes in the area of volunteered geographic information"
    - "investigating italian disinformation spreading on twitter in the context of 2019 european elections"
    - "historical comparison of gender inequality in scientific careers across countries and disciplines"
    - "text authorship identified using the dynamics of word co occurrence networks"
    - "a complex network approach to stylometry"
    - "probing the topological properties of complex networks modeling short written texts"

Connected through hyperedge HE-5 (induced by source paper "ideotrace a framework for ideology tracing with a case study on the 2016 u s presidential election"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "red bots do it better comparative analysis of social bot partisan behavior"
    - "predicting factuality of reporting and bias of news media sources"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "impact of social media posts in real life violence a case study in bangladesh"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "who falls for online political manipulation"
    - "a tutorial on modeling and analysis of dynamic social networks part i"

Connected through hyperedge HE-6 (induced by source paper "influence of fake news in twitter during the 2016 us presidential election"):
  with members:
    - "anatomy of an online misinformation network"
    - "the spread of low credibility content by social bots"
    - "limited individual attention and online virality of low quality information"
    - "online human bot interactions detection estimation and characterization"
    - "users polarization on facebook and youtube"
    - "hoaxy a platform for tracking online misinformation"
    - "the anatomy of brexit debate on facebook"
    - "the rise of social bots"

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)