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: "shaping social activity by incentivizing users"
  Abstract: Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.

Connected through hyperedge HE-1 (induced by source paper "self attentive hawkes processes"):
  with members:
    - "self attention with structural position representations"
    - "intensity free learning of temporal point processes"
    - "fully neural network based model for general temporal point processes"
    - "learning temporal point processes via reinforcement learning"
    - "self attention with relative position representations"
    - "benchmarking deep sequential models on volatility predictions for financial time series"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "non local neural networks"

Connected through hyperedge HE-2 (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-3 (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-4 (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-5 (induced by source paper "expecting to be hip hawkes intensity processes for social media popularity"):
  with members:
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "feature driven and point process approaches for popularity prediction"
    - "do cascades recur"
    - "exploring limits to prediction in complex social systems"
    - "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes"
    - "learning and forecasting opinion dynamics in social networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "modeling and predicting popularity dynamics via reinforced poisson processes"

Connected through hyperedge HE-6 (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"

Connected through hyperedge HE-7 (induced by source paper "know evolve deep temporal reasoning for dynamic knowledge graphs"):
  with members:
    - "probabilistic reasoning via deep learning neural association models"
    - "steering opinion dynamics in information diffusion networks"
    - "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"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-8 (induced by source paper "variational policy for guiding point processes"):
  with members:
    - "steering opinion dynamics in information diffusion networks"
    - "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"
    - "path integral policy improvement with covariance matrix adaptation"

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)