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: "diffusive logistic model towards predicting information diffusion in online social networks"
  Abstract: Online social networks have recently become an effective and innovative channel for spreading information and influence among hundreds of millions of end users. Many prior work have carried out empirical studies and proposed diffusion models to understand the information diffusion process in online social networks. However, most of these studies focus on the information diffusion in temporal dimension, that is, how the information propagates over time. Little attempt has been given on understanding information diffusion over both temporal and spatial dimensions. In this paper, we propose a Partial Differential Equation (PDE), specifically, a Diffusive Logistic (DL) equation to model the temporal and spatial characteristics of information diffusion in online social networks. To be more specific, we develop a PDE-based theoretical framework to measure and predict the density of influenced users at a given distance from the original information source after a time period. The density of influenced users over time and distance provides valuable insight on the actual information diffusion process. We present the temporal and spatial patterns in a real dataset collected from Digg social news site, and validate the proposed DL equation in terms of predicting the information diffusion process. Our experiment results show that the DL model is indeed able to characterize and predict the process of information propagation in online social networks. For example, for the most popular news with 24,099 votes in Digg, the average prediction accuracy of DL model over all distances during the first 6 hours is 92.08%. To the best of our knowledge, this paper is the first attempt to use PDE-based model to study the information diffusion process in both temporal and spatial dimensions in online social networks.

Connected through hyperedge HE-1 (induced by source paper "modeling information diffusion in online social networks with partial differential equations"):
  with members:
    - "information diffusion and external influence in networks"
    - "on the interplay between social and topical structure"
    - "what stops social epidemics"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "a framework for quantitative analysis of cascades on networks"

Connected through hyperedge HE-2 (induced by source paper "midmod osn a microscopic level information diffusion model for online social networks"):
  with members:
    - "predicting online extremism content adopters and interaction reciprocity"
    - "when to post on social networks"
    - "klout score measuring influence across multiple social networks"
    - "predicting the temporal dynamics of information diffusion in social networks"

Connected through hyperedge HE-3 (induced by source paper "iad interaction aware diffusion framework in social networks"):
  with members:
    - "quantifying the effect of sentiment on information diffusion in social media"
    - "anger is more influential than joy sentiment correlation in weibo"
    - "modeling adoption and usage of competing products"
    - "sketch based influence maximization and computation scaling up with guarantees"

Connected through hyperedge HE-4 (induced by source paper "blag bandit on large action set graph"):
  with members:
    - "can cascades be predicted"
    - "the role of social networks in information diffusion"
    - "from bandits to experts on the value of side observations"
    - "co evolution of content popularity and delivery in mobile p2p networks"

Connected through hyperedge HE-5 (induced by source paper "tweet for behavior change using social media for the dissemination of public health messages"):
  with members:
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "influence and passivity in social media"

Connected through hyperedge HE-6 (induced by source paper "inferring the underlying structure of information cascades"):
  with members:
    - "inferring networks of diffusion and influence"
    - "pricing strategies for viral marketing on 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)