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: "ultrametricity of information cascades"
  Abstract: Whether it is the inter-arrival time between two consecutive votes on a story on Reddit or the comments on a video shared on Youtube, there is always a hierarchy of time scales in information propagation. One vote/comment might occur almost simultaneously with the previous, whereas another vote/comment might occur hours after the preceding one. This hierarchy of time scales leads us to believe that information cascades can be modeled using ultrametricity and ultradiffusion.This paper reports an investigation into cascades of information flow underlying Reddit, Youtube and Digg. An information cascade represents the spread of information from one node to his friends, from friends to their friends of friends and so on. It might be impossible to completely perceive the entire process of information flow as some of the data pertaining to it might be hidden or inaccessible to us. However, we might be able to observe some counting process which is a consequence of this diffusion. For example, in Digg this counting process might be the temporal variation in the number of votes accrued by a story. In Youtube, it might be the number of comments received by a video with time.We study the dynamics of these votes and comments to better understand information spread.Our observations can be described by a universal function whose parameters depend upon the system under consideration. This function can be derived by using ultrametricity to describe the propagation. The parameters for the ultradiffusion process are learned from the actual observations. We demonstrate that the results predicted by simulating the ultradiffusion process are in close correspondence to the actual observations.

Connected through hyperedge HE-1 (induced by source paper "why do cascade sizes follow a power law"):
  with members:
    - "there is something beyond the twitter network"
    - "do cascades recur"
    - "algorithmic complexity of power law networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "what stops social epidemics"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-2 (induced by source paper "there is something beyond the twitter network"):
  with members:
    - "aggregate characterization of user behavior in twitter and analysis of the retweet graph"
    - "what stops social epidemics"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

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)