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 "correlated cascades compete or cooperate"):
  Abstract: In real world social networks, there are multiple cascades which are rarely independent. They usually compete or cooperate with each other. Motivated by the reinforcement theory in sociology we leverage the fact that adoption of a user to any behavior is modeled by the aggregation of behaviors of its neighbors. We use a multidimensional marked Hawkes process to model users product adoption and consequently spread of cascades in social networks. The resulting inference problem is proved to be convex and is solved in parallel by using the barrier method. The advantage of the proposed model is twofold; it models correlated cascades and also learns the latent diffusion network. Experimental results on synthetic and two real datasets gathered from Twitter, URL shortening and music streaming services, illustrate the superior performance of the proposed model over the alternatives.

Includes members:
  Member "multistage campaigning in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning with relational information for short sequences")
  Member "learning granger causality for hawkes processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning multivariate hawkes processes at scale")
  Member "back to the past source identification in diffusion networks from partially observed cascades":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cascade size distributions and why they matter")
  Member "the simple rules of social contagion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural diffusion model for microscopic cascade prediction")
  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 "from competition to complementarity comparative influence diffusion and maximization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback")
  Member "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  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)