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: "co evolution of selection and influence in social networks"
  Abstract: Many networks are complex dynamical systems, where both attributes of nodes and topology of the network (link structure) can change with time. We propose a model of co-evolving networks where both node at- tributes and network structure evolve under mutual influence. Specifically, we consider a mixed membership stochastic blockmodel, where the probability of observing a link between two nodes depends on their current membership vectors, while those membership vectors themselves evolve in the presence of a link between the nodes. Thus, the network is shaped by the interaction of stochastic processes describing the nodes, while the processes themselves are influenced by the changing network structure. We derive an efficient variational inference procedure for our model, and validate the model on both synthetic and real-world data.

Connected through hyperedge HE-1 (induced by source paper "use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making"):
  with members:
    - "leveraging motifs to model the temporal dynamics of diffusion networks"
    - "impact of social influence on adoption behavior an online controlled experimental evaluation"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "cumulative effect in information diffusion empirical study on a microblogging network"
    - "what stops social epidemics"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-2 (induced by source paper "capturing edge attributes via network embedding"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "dyngem deep embedding method for dynamic graphs"
    - "graph embedding techniques applications and performance a survey"
    - "struc2vec learning node representations from structural identity"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "graph2vec learning distributed representations of graphs"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "latent space model for multi modal social data"):
  with members:
    - "structure and inference in annotated networks"
    - "analysis of a heterogeneous social network of humans and cultural objects"
    - "quantifying the effect of sentiment on information diffusion in social media"
    - "on facebook most ties are weak"
    - "online popularity and topical interests through the lens of instagram"
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"
    - "traveling trends social butterflies or frequent fliers"

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)