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: "quotus the structure of political media coverage as revealed by quoting patterns"
  Abstract: Given the extremely large pool of events and stories available, media outlets need to focus on a subset of issues and aspects to convey to their audience. Outlets are often accused of exhibiting a systematic bias in this selection process, with different outlets portraying different versions of reality. However, in the absence of objective measures and empirical evidence, the direction and extent of systematicity remains widely disputed. #R##N#In this paper we propose a framework based on quoting patterns for quantifying and characterizing the degree to which media outlets exhibit systematic bias. We apply this framework to a massive dataset of news articles spanning the six years of Obama's presidency and all of his speeches, and reveal that a systematic pattern does indeed emerge from the outlet's quoting behavior. Moreover, we show that this pattern can be successfully exploited in an unsupervised prediction setting, to determine which new quotes an outlet will select to broadcast. By encoding bias patterns in a low-rank space we provide an analysis of the structure of political media coverage. This reveals a latent media bias space that aligns surprisingly well with political ideology and outlet type. A linguistic analysis exposes striking differences across these latent dimensions, showing how the different types of media outlets portray different realities even when reporting on the same events. For example, outlets mapped to the mainstream conservative side of the latent space focus on quotes that portray a presidential persona disproportionately characterized by negativity.

Connected through hyperedge HE-1 (induced by source paper "a framework for the computational linguistic analysis of dehumanization"):
  with members:
    - "social bias frames reasoning about social and power implications of language"
    - "mitigating gender bias in natural language processing literature review"
    - "talkdown a corpus for condescension detection in context"
    - "automatically neutralizing subjective bias in text"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "contextual affective analysis a case study of people portrayals in online metoo stories"
    - "entity centric contextual affective analysis"
    - "examining gender and race bias in two hundred sentiment analysis systems"

Connected through hyperedge HE-2 (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"
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"

Connected through hyperedge HE-3 (induced by source paper "no permanent friends or enemies tracking relationships between nations from news"):
  with members:
    - "framing and agenda setting in russian news a computational analysis of intricate political strategies"
    - "you are no jack kennedy on media selection of highlights from presidential debates"
    - "friendships rivalries and trysts characterizing relations between ideas in texts"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "rookie a unique approach for exploring news archives"
    - "inferring interpersonal relations in narrative summaries"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "you are no jack kennedy on media selection of highlights from presidential debates"):
  with members:
    - "talk it up or play it down un expected correlations between de emphasis and recurrence of discussion points in consequential u s economic policy meetings"
    - "conversational flow in oxford style debates"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "you had me at hello how phrasing affects memorability"
    - "more voices than ever quantifying media bias in networks"

Connected through hyperedge HE-5 (induced by source paper "asking too much the rhetorical role of questions in political discourse"):
  with members:
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "interpreting neural networks to improve politeness comprehension"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "how to ask for a favor a case study on the success of altruistic requests"
    - "a computational approach to politeness with application to social factors"
    - "scikit learn machine learning in python"
    - "get out the vote determining support or opposition from congressional floor debate transcripts"

Connected through hyperedge HE-6 (induced by source paper "intelligent disaster response via social media analysis a survey"):
  with members:
    - "finding eyewitness tweets during crises"
    - "processing social media messages in mass emergency a survey"
    - "a survey of prediction using social media"
    - "dynamical classes of collective attention in twitter"
    - "truthy mapping the spread of astroturf in microblog streams"
    - "predicting the popularity of online content"

Connected through hyperedge HE-7 (induced by source paper "friendships rivalries and trysts characterizing relations between ideas in texts"):
  with members:
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "community structure and interaction dynamics through the lens of quotes"):
  with members:
    - "measuring tie strength in implicit social networks"

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.CL(Computation and Language)