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: "stochastic models predict user behavior in social media"
  Abstract: User response to contributed content in online social media depends on many factors. These include how the site lays out new content, how frequently the user visits the site, how many friends the user follows, how active these friends are, as well as how interesting or useful the content is to the user. We present a stochastic modeling framework that relates a user's behavior to details of the site's user interface and user activity and describe a procedure for estimating model parameters from available data. We apply the model to study discussions of controversial topics on Twitter, specifically, to predict how followers of an advocate for a topic respond to the advocate's posts. We show that a model of user behavior that explicitly accounts for a user transitioning through a series of states before responding to an advocate's post better predicts response than models that fail to take these states into account. We demonstrate other benefits of stochastic models, such as their ability to identify users who are highly interested in advocate's posts.

Connected through hyperedge HE-1 (induced by source paper "continuous time user modeling in the presence of badges a probabilistic approach"):
  with members:
    - "spatio temporal modeling of users check ins in location based social networks"
    - "correlated cascades compete or cooperate"
    - "modeling adoption and usage of competing products"
    - "engaging with massive online courses"
    - "uncovering the temporal dynamics of diffusion networks"
    - "using stochastic models to describe and predict social dynamics of web users"
    - "on prediction using variable order markov models"

Connected through hyperedge HE-2 (induced by source paper "information is not a virus and other consequences of human cognitive limits"):
  with members:
    - "the majority illusion in social networks"
    - "user effort and network structure mediate access to information in networks"
    - "the simple rules of social contagion"
    - "portrait of an online shopper understanding and predicting consumer behavior"
    - "network weirdness exploring the origins of network paradoxes"
    - "friendship paradox redux your friends are more interesting than you"
    - "information diffusion and external influence in networks"
    - "social dynamics of digg"

Connected through hyperedge HE-3 (induced by source paper "vip incorporating human cognitive biases in a probabilistic model of retweeting"):
  with members:
    - "can cascades be predicted"
    - "virality prediction and community structure in social networks"
    - "la ctr a limited attention collaborative topic regression for social media"
    - "quantifying long term scientific impact"
    - "the role of social networks in information diffusion"
    - "social dynamics of digg"

Connected through hyperedge HE-4 (induced by source paper "user effort and network structure mediate access to information in networks"):
  with members:
    - "vip incorporating human cognitive biases in a probabilistic model of retweeting"
    - "structural and cognitive bottlenecks to information access in social networks"
    - "la ctr a limited attention collaborative topic regression for social media"
    - "the role of social networks in information diffusion"
    - "collaborative topic regression with social matrix factorization for recommendation systems"
    - "social dynamics of digg"

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.CY(Computers and Society)