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: "a stochastic differential equation framework for guiding online user activities in closed loop"
  Abstract: Recently, there is a surge of interest in using point processes to model continuous-time user activities. This framework has resulted in novel models and improved performance in diverse applications. However, most previous works focus on the "open loop" setting where learned models are used for predictive tasks. Typically, we are interested in the "closed loop" setting where a policy needs to be learned to incorporate user feedbacks and guide user activities to desirable states. Although point processes have good predictive performance, it is not clear how to use them for the challenging closed loop activity guiding task. In this paper, we propose a framework to reformulate point processes into stochastic differential equations, which allows us to extend methods from stochastic optimal control to address the activity guiding problem. We also design an efficient algorithm, and show that our method guides user activities to desired states more effectively than the state of the art.

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 "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"
    - "steering opinion dynamics in information diffusion networks"
    - "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 "steering opinion dynamics in information diffusion networks"):
  with members:
    - "action selection in growing state spaces control of network structure growth"
    - "node2vec scalable feature learning for networks"
    - "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"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "shaping social activity by incentivizing users"
    - "modeling information propagation with survival theory"

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)