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: "the simple rules of social contagion"
  Abstract: It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior.

Connected through hyperedge HE-1 (induced by source paper "neural diffusion model for microscopic cascade prediction"):
  with members:
    - "attention is all you need"
    - "who will share my image predicting the content diffusion path in online social networks"
    - "topological recurrent neural network for diffusion prediction"
    - "semi supervised classification with graph convolutional networks"
    - "deepcas an end to end predictor of information cascades"
    - "deep residual learning for image recognition"
    - "line large scale information network embedding"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-2 (induced by source paper "multi winner contests for strategic diffusion in social networks"):
  with members:
    - "selling multiple items via social networks"
    - "information design in crowdfunding under thresholding policies"
    - "an online mechanism for ridesharing in autonomous mobility on demand systems"
    - "the rise of social bots"
    - "social dynamics of digg"
    - "time critical social mobilization"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-3 (induced by source paper "can you trust the trend discovering simpson s paradoxes in social data"):
  with members:
    - "on quitting performance and practice in online game play"
    - "information is not a virus and other consequences of human cognitive limits"
    - "evidence of online performance deterioration in user sessions on reddit"
    - "averaging gone wrong using time aware analyses to better understand behavior"
    - "what stops social epidemics"

Connected through hyperedge HE-4 (induced by source paper "predicting and explaining behavioral data with structured feature space decomposition"):
  with members:
    - "can you trust the trend discovering simpson s paradoxes in social data"
    - "the mythos of model interpretability"
    - "popularity and quality in social news aggregators a study of reddit and hacker news"
    - "what stops social epidemics"
    - "rule based machine learning methods for functional prediction"

Connected through hyperedge HE-5 (induced by source paper "model of cognitive dynamics predicts performance on standardized tests"):
  with members:
    - "dynamics of content quality in collaborative knowledge production"
    - "evidence of online performance deterioration in user sessions on reddit"
    - "on entropy for mixtures of discrete and continuous variables"

Connected through hyperedge HE-6 (induced by source paper "topological recurrent neural network for diffusion prediction"):
  with members:
    - "catch em all locating multiple diffusion sources in networks with partial observations"
    - "recurrent neural network grammars"
    - "node2vec scalable feature learning for networks"
    - "deepcas an end to end predictor of information cascades"
    - "effective approaches to attention based neural machine translation"
    - "improved semantic representations from tree structured long short term memory networks"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "computational social scientist beware simpson s paradox in behavioral data"):
  with members:
    - "can you trust the trend discovering simpson s paradoxes in social data"
    - "dynamics of content quality in collaborative knowledge production"
    - "information is not a virus and other consequences of human cognitive limits"
    - "evidence of online performance deterioration in user sessions on reddit"
    - "averaging gone wrong using time aware analyses to better understand behavior"
    - "portrait of an online shopper understanding and predicting consumer behavior"
    - "social dynamics of digg"
    - "what stops social epidemics"

Connected through hyperedge HE-8 (induced by source paper "bounded rationality in scholarly knowledge discovery"):
  with members:
    - "information is not a virus and other consequences of human cognitive limits"
    - "dynamical classes of collective attention in twitter"

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)