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: "quantifying search bias investigating sources of bias for political searches in social media"
  Abstract: Search systems in online social media sites are frequently used to find information about ongoing events and people. For topics with multiple competing perspectives, such as political events or political candidates, bias in the top ranked results significantly shapes public opinion. However, bias does not emerge from an algorithm alone. It is important to distinguish between the bias that arises from the data that serves as the input to the ranking system and the bias that arises from the ranking system itself. In this paper, we propose a framework to quantify these distinct biases and apply this framework to politics-related queries on Twitter. We found that both the input data and the ranking system contribute significantly to produce varying amounts of bias in the search results and in different ways. We discuss the consequences of these biases and possible mechanisms to signal this bias in social media search systems' interfaces.

Connected through hyperedge HE-1 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements"):
  with members:
    - "plug and play language models a simple approach to controlled text generation"
    - "generalizing from a few examples a survey on few shot learning"
    - "ctrl a conditional transformer language model for controllable generation"
    - "release strategies and the social impacts of language models"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "defending against neural fake news"
    - "studying fake news via network analysis detection and mitigation"
    - "hierarchical propagation networks for fake news detection investigation and exploitation"

Connected through hyperedge HE-2 (induced by source paper "the covid 19 social media infodemic"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "polarization and fake news early warning of potential misinformation targets"
    - "polarization of the vaccination debate on facebook"
    - "modeling the infectiousness of twitter hashtags"
    - "debunking in a world of tribes"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "facebook ads monitor an independent auditing system for political ads on facebook"):
  with members:
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "inside the right leaning echo chambers characterizing gab an unmoderated social system"
    - "when politicians talk about politics identifying political tweets of brazilian congressmen"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "portuguese word embeddings evaluating on word analogies and natural language tasks"
    - "participatory militias an analysis of an armed movement s online audience"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "a survey on computational politics"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "tweeting mps digital engagement between citizens and members of parliament in the uk"
    - "political discussions in homogeneous and cross cutting communication spaces"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "mining the demographics of political sentiment from twitter using learning from label proportions"
    - "words are malleable computing semantic shifts in political and media discourse"

Connected through hyperedge HE-5 (induced by source paper "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"):
  with members:
    - "quantifying the impact of user attention on fair group representation in ranked lists"
    - "preference informed fairness"
    - "facebook s advertising platform new attack vectors and the need for interventions"
    - "fairness under composition"
    - "myadchoices bringing transparency and control to online advertising"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"
    - "xray enhancing the web s transparency with differential correlation"

Connected through hyperedge HE-6 (induced by source paper "quantifying the impact of user attention on fair group representation in ranked lists"):
  with members:
    - "fairness in online social network timelines measurements models and mechanism design"
    - "equity of attention amortizing individual fairness in rankings"
    - "fairness of exposure in rankings"
    - "ranking with fairness constraints"
    - "fa ir a fair top k ranking algorithm"
    - "unbiased learning to rank with biased feedback"
    - "measuring fairness in ranked outputs"
    - "what makes a link successful on wikipedia"

Connected through hyperedge HE-7 (induced by source paper "the role of user profile for fake news detection"):
  with members:
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "fake news detection on social media a data mining perspective"
    - "csi a hybrid deep model for fake news detection"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"
    - "pr2 a language independent unsupervised tool for personality recognition from text"

Connected through hyperedge HE-8 (induced by source paper "measuring the importance of user generated content to search engines"):
  with members:
    - "botornot a system to evaluate social bots"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "not at home on the range peer production and the urban rural divide"
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "the tower of babel meets web 2 0 user generated content and its applications in a multilingual context"
    - "query chains learning to rank from implicit feedback"

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)