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: "information evolution in social networks"
  Abstract: Social networks readily transmit information, albeit with less than perfect fidelity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities. A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer "laterally" between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks.

Connected through hyperedge HE-1 (induced by source paper "message distortion in information cascades"):
  with members:
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "a corpus with multi level annotations of patients interventions and outcomes to support language processing for medical literature"
    - "how constraints affect content the case of twitter s switch from 140 to 280 characters"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "fake news detection in social networks via crowd signals"
    - "online human bot interactions detection estimation and characterization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-2 (induced by source paper "memesequencer sparse matching for embedding image macros"):
  with members:
    - "modeling image virality with pairwise spatial transformer networks"
    - "do cascades recur"
    - "a corpus and evaluation framework for deeper understanding of commonsense stories"
    - "end to end localization and ranking for relative attributes"
    - "tensorflow a system for large scale machine learning"
    - "understanding image virality"
    - "skip thought vectors"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "on the origins of memes by means of fringe web communities"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "memesequencer sparse matching for embedding image macros"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "on the efficiency of the information networks in social media"
    - "tensorflow a system for large scale machine learning"
    - "predicting successful memes using network and community structure"
    - "clustering memes in social media"
    - "truthy mapping the spread of astroturf in microblog streams"

Connected through hyperedge HE-4 (induced by source paper "do diffusion protocols govern cascade growth"):
  with members:
    - "can cascades be predicted"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-5 (induced by source paper "do cascades recur"):
  with members:
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"
    - "the untold story of the clones content agnostic factors that impact youtube video popularity"
    - "the role of social networks in information diffusion"
    - "information diffusion and external influence in networks"
    - "trends in social media persistence and decay"

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)