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: "measuring online social bubbles"
  Abstract: Social media have quickly become a prevalent channel to access information, spread ideas, and influence opinions. However, it has been suggested that social and algorithmic filtering may cause exposure to less diverse points of view, and even foster polarization and misinformation. Here we explore and validate this hypothesis quantitatively for the first time, at the collective and individual levels, by mining three massive datasets of web traffic, search logs, and Twitter posts. Our analysis shows that collectively, people access information from a significantly narrower spectrum of sources through social media and email, compared to search. The significance of this finding for individual exposure is revealed by investigating the relationship between the diversity of information sources experienced by users at the collective and individual level. There is a strong correlation between collective and individual diversity, supporting the notion that when we use social media we find ourselves inside "social bubbles". Our results could lead to a deeper understanding of how technology biases our exposure to new information.

Connected through hyperedge HE-1 (induced by source paper "positive algorithmic bias cannot stop fragmentation in homophilic social networks"):
  with members:
    - "on the inevitability of online echo chambers"
    - "understanding filter bubbles and polarization in social networks"
    - "exponential segregation in a two dimensional schelling model with tolerant individuals"
    - "unperturbed schelling segregation in two or three dimensions"
    - "digital morphogenesis via schelling segregation"
    - "an analysis of one dimensional schelling segregation"

Connected through hyperedge HE-2 (induced by source paper "measuring diversity in heterogeneous information networks"):
  with members:
    - "exploring author gender in book rating and recommendation"
    - "heterogeneous information network embedding for recommendation"
    - "a survey of heterogeneous information network analysis"
    - "renyi divergence and kullback leibler divergence"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-3 (induced by source paper "on the inevitability of online echo chambers"):
  with members:
    - "quantifying biases in online information exposure"
    - "how algorithmic popularity bias hinders or promotes quality"
    - "limited individual attention and online virality of low quality information"
    - "explaining the emergence of echo chambers on social media the role of ideology and extremism"
    - "the role of information diffusion in the evolution of social networks"
    - "partisan asymmetries in online political activity"
    - "happiness is assortative in online social networks"
    - "topical interests and the mitigation of search engine bias"

Connected through hyperedge HE-4 (induced by source paper "false information on web and social media a survey"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "the spread of low credibility content by social bots"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "an army of me sockpuppets in online discussion communities"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "automatic detection of fake news"
    - "beyond news contents the role of social context for fake news detection"

Connected through hyperedge HE-5 (induced by source paper "the spread of low credibility content by social bots"):
  with members:
    - "anatomy of an online misinformation network"
    - "detection and resolution of rumours in social media a survey"
    - "quantifying biases in online information exposure"
    - "how algorithmic popularity bias hinders or promotes quality"
    - "fake news detection on social media a data mining perspective"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"
    - "limited individual attention and online virality of low quality information"

Connected through hyperedge HE-6 (induced by source paper "quantifying biases in online information exposure"):
  with members:
    - "rating effects on social news posts and comments"
    - "hoaxy a platform for tracking online misinformation"
    - "the rise of social bots"
    - "data portraits connecting people of opposing views"
    - "topical interests and the mitigation of search engine bias"

Connected through hyperedge HE-7 (induced by source paper "me my echo chamber and i introspection on social media polarization"):
  with members:
    - "a long term analysis of polarization on twitter"
    - "automatic detection and categorization of election related tweets"
    - "reducing controversy by connecting opposing views"
    - "designing and deploying online field experiments"

Connected through hyperedge HE-8 (induced by source paper "the spread of fake news by social bots"):
  with members:
    - "how algorithmic popularity bias hinders or promotes quality"
    - "limited individual attention and online virality of low quality information"
    - "online human bot interactions detection estimation and characterization"
    - "hoaxy a platform for tracking online misinformation"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "the rise of social bots"
    - "partisan asymmetries in online political activity"

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)