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 "use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making"):
  Abstract: It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network, but it is unclear if these same principles hold when the "pattern" by it receives these signals vary and when potential decisions have different utilities. To investigate that, we manipulate social signal exposure in an online controlled experiment with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how social influence affects an individual's uncertainty which cannot always be observed in experiments of decision making, we use our behavioral findings to simulate dynamics in real world case studies through multi-agent models. We specifically try to measure the effect of the influence decision towards suboptimal choice adoption in real world diffusion; in this case rumor spreading. Our simulation results indicate that while sequential peer effects from the influence decision overcome individual uncertainty to guide faster diffusion over time, the control that peer influence has over decision making can itself be affected when influence is delayed and user uncertainty comes into play.

Includes members:
  Member "leveraging motifs to model the temporal dynamics of diffusion networks":
    also connected through other hyperedges:
      (none)
  Member "impact of social influence on adoption behavior an online controlled experimental evaluation":
    also connected through other hyperedges:
      (none)
  Member "analysing how people orient to and spread rumours in social media by looking at conversational threads":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "two path deep semi supervised learning for timely fake news detection")
  Member "cumulative effect in information diffusion empirical study on a microblogging network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "impact of social influence on adoption behavior an online controlled experimental evaluation")
  Member "what stops social epidemics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modeling information diffusion in online social networks with partial differential equations")
  Member "co evolution of selection and influence in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capturing edge attributes via network embedding")
  Member "a data based approach to social influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computational aspects of optimal strategic network diffusion")

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)