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: "modeling adoption and usage of competing products"
  Abstract: The emergence and wide-spread use of online social networks has led to a dramatic increase on the availability of social activity data. Importantly, this data can be exploited to investigate, at a microscopic level, some of the problems that have captured the attention of economists, marketers and sociologists for decades, such as, e.g., product adoption, usage and competition. #R##N#In this paper, we propose a continuous-time probabilistic model, based on temporal point processes, for the adoption and frequency of use of competing products, where the frequency of use of one product can be modulated by those of others. This model allows us to efficiently simulate the adoption and recurrent usages of competing products, and generate traces in which we can easily recognize the effect of social influence, recency and competition. We then develop an inference method to efficiently fit the model parameters by solving a convex program. The problem decouples into a collection of smaller subproblems, thus scaling easily to networks with hundred of thousands of nodes. We validate our model over synthetic and real diffusion data gathered from Twitter, and show that the proposed model does not only provides a good fit to the data and more accurate predictions than alternatives but also provides interpretable model parameters, which allow us to gain insights into some of the factors driving product adoption and frequency of use.

Connected through hyperedge HE-1 (induced by source paper "neural jump stochastic differential equations"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "diffeqflux jl a julia library for neural differential equations"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "solving for high dimensional committor functions using artificial neural networks"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "expecting to be hip hawkes intensity processes for social media popularity"

Connected through hyperedge HE-2 (induced by source paper "iad interaction aware diffusion framework in social networks"):
  with members:
    - "quantifying the effect of sentiment on information diffusion in social media"
    - "anger is more influential than joy sentiment correlation in weibo"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "diffusive logistic model towards predicting information diffusion in online social networks"

Connected through hyperedge HE-3 (induced by source paper "maximizing the diversity of exposure in a social network"):
  with members:
    - "finding low tension communities"
    - "minimizing polarization and disagreement in social networks"
    - "balancing information exposure in social networks"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "revenue maximization in incentivized social advertising"
    - "reducing controversy by connecting opposing views"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-4 (induced by source paper "modeling interdependent and periodic real world action sequences"):
  with members:
    - "how gamification affects physical activity large scale analysis of walking challenges in a mobile application"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "harnessing the web for population scale physiological sensing a case study of sleep and performance"
    - "online actions with offline impact how online social networks influence online and offline user behavior"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "portrait of an online shopper understanding and predicting consumer behavior"

Connected through hyperedge HE-5 (induced by source paper "distilling information reliability and source trustworthiness from digital traces"):
  with members:
    - "uncovering the dynamics of crowdlearning and the value of knowledge"
    - "multistage campaigning in social networks"
    - "assessing human error against a benchmark of perfection"
    - "smart broadcasting do you want to be seen"
    - "from data fusion to knowledge fusion"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "learning and forecasting opinion dynamics in social networks"
    - "knowledge based trust estimating the trustworthiness of web sources"

Connected through hyperedge HE-6 (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"
    - "engaging with massive online courses"
    - "stochastic models predict user behavior in social media"
    - "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-7 (induced by source paper "recurrent poisson factorization for temporal recommendation"):
  with members:
    - "continuous time user modeling in the presence of badges a probabilistic approach"
    - "spatio temporal modeling of users check ins in location based social networks"
    - "correlated cascades compete or cooperate"
    - "dynamic poisson factorization"
    - "modeling events with cascades of poisson processes"

Connected through hyperedge HE-8 (induced by source paper "uncovering the dynamics of crowdlearning and the value of knowledge"):
  with members:
    - "shaping social activity by incentivizing users"
    - "engaging with massive online courses"
    - "efficient learning of generalized linear and single index models with isotonic regression"

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)