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: "la ctr a limited attention collaborative topic regression for social media"
  Abstract: Probabilistic models can learn users' preferences from the history of their item adoptions on a social media site, and in turn, recommend new items to users based on learned preferences. However, current models ignore psychological factors that play an important role in shaping online social behavior. One such factor is attention, the mechanism that integrates perceptual and cognitive features to select the items the user will consciously process and may eventually adopt. Recent research has shown that people have finite attention, which constrains their online interactions, and that they divide their limited attention non-uniformly over other people. We propose a collaborative topic regression model that incorporates limited, non-uniformly divided attention. We show that the proposed model is able to learn more accurate user preferences than state-of-art models, which do not take human cognitive factors into account. Specifically we analyze voting on news items on the social news aggregator and show that our model is better able to predict held out votes than alternate models. Our study demonstrates that psycho-socially motivated models are better able to describe and predict observed behavior than models which only consider latent social structure and content.

Connected through hyperedge HE-1 (induced by source paper "tribeflow mining predicting user trajectories"):
  with members:
    - "a scalable asynchronous distributed algorithm for topic modeling"
    - "revisit behavior in social media the phoenix r model and discoveries"
    - "dj mc a reinforcement learning agent for music playlist recommendation"
    - "scalable recommendation with poisson factorization"
    - "la lda a limited attention topic model for social recommendation"
    - "community detection in networks with node attributes"
    - "supervised random walks predicting and recommending links in social networks"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-2 (induced by source paper "vip incorporating human cognitive biases in a probabilistic model of retweeting"):
  with members:
    - "can cascades be predicted"
    - "stochastic models predict user behavior in social media"
    - "virality prediction and community structure in social networks"
    - "quantifying long term scientific impact"
    - "the role of social networks in information diffusion"
    - "social dynamics of digg"

Connected through hyperedge HE-3 (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"
    - "stochastic models predict user behavior in social media"
    - "structural and cognitive bottlenecks to information access in social networks"
    - "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.IR(Information Retrieval)