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: "social contagion an empirical study of information spread on digg and twitter follower graphs"
  Abstract: Social networks have emerged as a critical factor in information dissemination, search, marketing, expertise and influence discovery, and potentially an important tool for mobilizing people. Social media has made social networks ubiquitous, and also given researchers access to massive quantities of data for empirical analysis. These data sets offer a rich source of evidence for studying dynamics of individual and group behavior, the structure of networks and global patterns of the flow of information on them. However, in most previous studies, the structure of the underlying networks was not directly visible but had to be inferred from the flow of information from one individual to another. As a result, we do not yet understand dynamics of information spread on networks or how the structure of the network affects it. We address this gap by analyzing data from two popular social news sites. Specifically, we extract follower graphs of active Digg and Twitter users and track how interest in news stories cascades through the graph. We compare and contrast properties of information cascades on both sites and elucidate what they tell us about dynamics of information flow on networks.

Connected through hyperedge HE-1 (induced by source paper "when less is more systematic analysis of cascade based community detection"):
  with members:
    - "systematic analysis of cluster similarity indices towards bias free cluster validation"
    - "community detection through likelihood optimization in search of a sound model"
    - "dani a fast diffusion aware network inference algorithm"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "metrics for community analysis a survey"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "modeling information propagation with survival theory"
    - "structure and dynamics of information pathways in online media"

Connected through hyperedge HE-2 (induced by source paper "influence maximization for social good use of social networks in low resource communities"):
  with members:
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "community structure and scale free collections of erdős renyi graphs"
    - "information theoretic measures of influence based on content dynamics"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "community detection using a measure of global influence"

Connected through hyperedge HE-3 (induced by source paper "models of continuous time networks with tie decay diffusion and convection"):
  with members:
    - "tempus volat hora fugit a survey of dynamic network models in discrete and continuous time"
    - "information is not a virus and other consequences of human cognitive limits"

Connected through hyperedge HE-4 (induced by source paper "the essential role of time in network based recommendation"):
  with members:
    - "do recommender systems benefit users a modeling approach"
    - "information filtering by similarity preferential diffusion processes"
    - "trend prediction in temporal bipartite networks the case of movielens netflix and digg"

Connected through hyperedge HE-5 (induced by source paper "network infusion to infer information sources in networks"):
  with members:
    - "back to the past source identification in diffusion networks from partially observed cascades"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-6 (induced by source paper "anticipating activity in social media spikes"):
  with members:
    - "rising tides or rising stars dynamics of shared attention on twitter during media events"
    - "the role of social networks in information diffusion"
    - "dynamical classes of collective attention in twitter"

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)