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: "action selection in growing state spaces control of network structure growth"
  Abstract: This project is co-financed by the Marie Curie FP7-PEOPLE-2012-COFUND Action, Grant agreement#R##N#no: 600387, the Marie Curie Initial Training Network NETT, project N. 289146 and the Spanish#R##N#Ministry of Economy and Competitiveness under the Mar´ia de Maeztu Units of Excellence Programme#R##N#(MDM-2015-0502).

Connected through hyperedge HE-1 (induced by source paper "learning to optimize via wasserstein deep inverse optimal control"):
  with members:
    - "generative adversarial imitation learning"
    - "model free imitation learning with policy optimization"
    - "a connection between generative adversarial networks inverse reinforcement learning and energy based models"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "maximum entropy deep inverse reinforcement learning"
    - "adam a method for stochastic optimization"
    - "continuous inverse optimal control with locally optimal examples"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-2 (induced by source paper "fast rates for online learning in linearly solvable markov decision processes"):
  with members:
    - "introduction to online convex optimization"
    - "fast rates in statistical and online learning"
    - "real time stochastic optimal control for multi agent quadrotor systems"
    - "latent kullback leibler control for continuous state systems using probabilistic graphical models"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

Connected through hyperedge HE-3 (induced by source paper "steering opinion dynamics in information diffusion networks"):
  with members:
    - "a stochastic differential equation framework for guiding online user activities in closed loop"
    - "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)