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: "steering opinion dynamics in information diffusion networks"
  Abstract: Our opinion is influenced by those of others we connect to in social networks. How to model opinion dynamics across social networks and can we steer the public opinion to a desired state? Answers to these questions are critically important for understanding the vulnerabilities of online social networks and increasing their resilience to rumor and false information. Recently there has been much work on modeling the dynamics in information diffusion, but few work has integrated these models into a systematic control framework. In this paper, we propose a unified multivariate jump diffusion process framework for modeling opinion dynamics over networks and determining the control over such networks. Our method can handle noisy and partially observed data, and over networks with time-varying and node birth processes. Using synthetic and real world networks, we showed that our framework is robust, able to control both stable and unstable dynamics systems with fast convergence speed, less variance and low control cost.

Connected through hyperedge HE-1 (induced by source paper "the mass fake news and cognition security"):
  with members:
    - "the role of user profile for fake news detection"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "studying fake news via network analysis detection and mitigation"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "deepinf social influence prediction with deep learning"

Connected through hyperedge HE-2 (induced by source paper "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "a stochastic differential equation framework for guiding online user activities in closed loop"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "feature driven and point process approaches for popularity prediction"
    - "exploring limits to prediction in complex social systems"
    - "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes"
    - "from micro to macro uncovering and predicting information cascading process with behavioral dynamics"

Connected through hyperedge HE-3 (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"
    - "detecting weak changes in dynamic events over networks"
    - "learning granger causality for hawkes processes"
    - "smart broadcasting do you want to be seen"
    - "correlated cascades compete or cooperate"

Connected through hyperedge HE-4 (induced by source paper "know evolve deep temporal reasoning for dynamic knowledge graphs"):
  with members:
    - "probabilistic reasoning via deep learning neural association models"
    - "discriminative embeddings of latent variable models for structured data"
    - "deep coevolutionary network embedding user and item features for recommendation"
    - "session based recommendations with recurrent neural networks"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "predicting the co evolution of event and knowledge graphs"
    - "shaping social activity by incentivizing users"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-5 (induced by source paper "variational policy for guiding point processes"):
  with members:
    - "fast and simple optimization for poisson likelihood models"
    - "smart broadcasting do you want to be seen"
    - "deep coevolutionary network embedding user and item features for recommendation"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "learning and forecasting opinion dynamics in social networks"
    - "shaping social activity by incentivizing users"
    - "path integral policy improvement with covariance matrix adaptation"

Connected through hyperedge HE-6 (induced by source paper "multistage campaigning in social networks"):
  with members:
    - "campaigning in heterogeneous social networks optimal control of si information epidemics"
    - "learning granger causality for hawkes processes"
    - "smart broadcasting do you want to be seen"
    - "modeling opinion dynamics in diffusion networks"
    - "influence prediction for continuous time information propagation on networks"
    - "learning and forecasting opinion dynamics in social networks"
    - "shaping social activity by incentivizing users"
    - "modeling adoption and usage of competing products"

Connected through hyperedge HE-7 (induced by source paper "learning and forecasting opinion dynamics in social networks"):
  with members:
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "shaping social activity by incentivizing users"
    - "modeling adoption and usage of competing products"
    - "how bad is forming your own opinion"
    - "uncovering the temporal dynamics of diffusion networks"

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)