human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "action selection in growing state spaces control of network structure growth":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to optimize via wasserstein deep inverse optimal control")
  Member "a stochastic differential equation framework for guiding online user activities in closed loop":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural jump stochastic differential equations")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep graph matching consensus")
  Member "deep coevolutionary network embedding user and item features for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predicting dynamic embedding trajectory in temporal interaction networks")
  Member "coevolve a joint point process model for information diffusion and network co evolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "predicting dynamic embedding trajectory in temporal interaction networks")
  Member "learning and forecasting opinion dynamics in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "consequential ranking algorithms and long term welfare")
  Member "seismic a self exciting point process model for predicting tweet popularity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction")
  Member "shaping social activity by incentivizing users":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "self attentive hawkes processes")

Question: Please tell me which class the source paper 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)