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: "correlated cascades compete or cooperate"
  Abstract: In real world social networks, there are multiple cascades which are rarely independent. They usually compete or cooperate with each other. Motivated by the reinforcement theory in sociology we leverage the fact that adoption of a user to any behavior is modeled by the aggregation of behaviors of its neighbors. We use a multidimensional marked Hawkes process to model users product adoption and consequently spread of cascades in social networks. The resulting inference problem is proved to be convex and is solved in parallel by using the barrier method. The advantage of the proposed model is twofold; it models correlated cascades and also learns the latent diffusion network. Experimental results on synthetic and two real datasets gathered from Twitter, URL shortening and music streaming services, illustrate the superior performance of the proposed model over the alternatives.

Connected through hyperedge HE-1 (induced by source paper "meta learning with relational information for short sequences"):
  with members:
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "probabilistic model agnostic meta learning"
    - "motifs in temporal networks"
    - "prototypical networks for few shot learning"
    - "fake news mitigation via point process based intervention"
    - "a dirichlet mixture model of hawkes processes for event sequence clustering"

Connected through hyperedge HE-2 (induced by source paper "estimating attention flow in online video networks"):
  with members:
    - "offline evaluation of ranking policies with click models"
    - "top k off policy correction for a reinforce recommender system"
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "foundations of sequence to sequence modeling for time series"
    - "beyond views measuring and predicting engagement on youtube videos"
    - "online popularity under promotion viral potential forecasting and the economics of time"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "do cascades recur"

Connected through hyperedge HE-3 (induced by source paper "learning graphs from noisy epidemic cascades"):
  with members:
    - "the cost of uncertainty in curing epidemics"
    - "fake news mitigation via point process based intervention"
    - "hiding the rumor source"
    - "a lower bound on the performance of dynamic curing policies for epidemics on graphs"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "spy vs spy rumor source obfuscation"
    - "an efficient curing policy for epidemics on graphs"
    - "can cascades be predicted"

Connected through hyperedge HE-4 (induced by source paper "on multi cascade influence maximization model hardness and algorithmic framework"):
  with members:
    - "on misinformation containment in online social networks"
    - "balancing information exposure in social networks"
    - "distributed rumor blocking with multiple positive cascades"
    - "an efficient randomized algorithm for rumor blocking in online social networks"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-5 (induced by source paper "choracle a unified statistical framework for churn prediction"):
  with members:
    - "recurrent poisson factorization for temporal recommendation"
    - "wasserstein learning of deep generative point process models"
    - "tensorflow a system for large scale machine learning"
    - "behavioral modeling for churn prediction early indicators and accurate predictors of custom defection and loyalty"
    - "a recurrent latent variable model for sequential data"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "co contagion diffusion on multilayer networks"):
  with members:
    - "complex contagions a decade in review"
    - "co diffusion of social contagions"
    - "a picture is worth a thousand words an empirical study on the influence of content visibility on diffusion processes within a virtual world"
    - "graphical evolutionary game for information diffusion over social networks"
    - "a game theoretical approach to broadcast information diffusion in social networks"

Connected through hyperedge HE-7 (induced by source paper "representation learning over dynamic graphs"):
  with members:
    - "dyngem deep embedding method for dynamic graphs"
    - "graph clustering with dynamic embedding"
    - "motifs in temporal networks"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "representation learning on graphs methods and applications"
    - "fake news mitigation via point process based intervention"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-8 (induced by source paper "fake news mitigation via point process based intervention"):
  with members:
    - "recurrent poisson factorization for temporal recommendation"
    - "distilling information reliability and source trustworthiness from digital traces"
    - "wasserstein learning of deep generative point process models"
    - "multistage campaigning in social networks"
    - "steering opinion dynamics in information diffusion networks"
    - "detecting weak changes in dynamic events over networks"
    - "learning granger causality for hawkes processes"
    - "smart broadcasting do you want to be seen"

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)