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: "impact of social influence on adoption behavior an online controlled experimental evaluation"
  Abstract: It is widely believed that the adoption behavior of a decision-maker in a social network is related to the number of signals it receives from its peers in the social network. It is unclear if these same principles hold when the "pattern" by which they receive 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. Specifically, we change the number of signals and the pattern through which participants receive them over time. We analyze its effect through a controlled game where each participant makes a decision to select one option when presented with six choices with differing utilities, with one choice having the most utility. We avoided network effects by holding the neighborhood network of the users constant. Over multiple rounds of the game, we observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the six choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar choices, (2) when the quantity of social signals vary over time, the probability that a participant selects the decision similar to the one reflected by the social signals and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals and (3) an early subjugation to higher quantity of peer social signals turned out to be a more effective strategy of social influence when aggregated over the rounds.

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"
    - "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"
    - "co evolution of selection and influence in social networks"
    - "a data based approach to social influence maximization"

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)