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: "composite social network for predicting mobile apps installation"
  Abstract: We have carefully instrumented a large portion of the population living in a university graduate dormitory by giving participants Android smart phones running our sensing software. In this paper, we propose the novel problem of predicting mobile application (known as "apps") installation using social networks and explain its challenge. Modern smart phones, like the ones used in our study, are able to collect different social networks using built-in sensors. (e.g. Bluetooth proximity network, call log network, etc) While this information is accessible to app market makers such as the iPhone AppStore, it has not yet been studied how app market makers can use these information for marketing research and strategy development. We develop a simple computational model to better predict app installation by using a composite network computed from the different networks sensed by phones. Our model also captures individual variance and exogenous factors in app adoption. We show the importance of considering all these factors in predicting app installations, and we observe the surprising result that app installation is indeed predictable. We also show that our model achieves the best results compared with generic approaches: our results are four times better than random guess, and predict almost 45% of all apps users install with almost 45% precision (F1 score= 0.43).

Connected through hyperedge HE-1 (induced by source paper "pocketcare tracking the flu with mobile phones using partial observations of proximity and symptoms"):
  with members:
    - "open badges a low cost toolkit for measuring team communication and dynamics"
    - "beyond contagion reality mining reveals complex patterns of social influence"
    - "measuring large scale social networks with high resolution"
    - "graph coupled hmms for modeling the spread of infection"

Connected through hyperedge HE-2 (induced by source paper "apps places and people strategies limitations and trade offs in the physical and digital worlds"):
  with members:
    - "smartphone app usage prediction using points of interest"

Connected through hyperedge HE-3 (induced by source paper "the lifecycles of apps in a social ecosystem"):
  with members:
    - "can cascades be predicted"
    - "virality prediction and community structure in social networks"
    - "the role of social networks in information diffusion"

Connected through hyperedge HE-4 (induced by source paper "friendship prediction in composite social networks"):
  with members:
    - "the nonparametric metadata dependent relational model"
    - "topic models conditioned on arbitrary features with dirichlet multinomial regression"
    - "supervised random walks predicting and recommending links in social networks"
    - "domain adaptation for statistical classifiers"

Connected through hyperedge HE-5 (induced by source paper "predicting adoption probabilities in social networks"):
  with members:
    - "the pulse of news in social media forecasting popularity"
    - "locally weighted naive bayes"
    - "time critical social mobilization"
    - "trends prediction using social diffusion models"

Connected through hyperedge HE-6 (induced by source paper "trends prediction using social diffusion models"):
  with members:
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-7 (induced by source paper "incremental learning with accuracy prediction of social and individual properties from mobile phone data"):
  with members:
    - "community detection using a neighborhood strength driven label propagation algorithm"

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)