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: "full scale cascade dynamics prediction with a local first approach"
  Abstract: Information cascades are ubiquitous in various social networking web sites. What mechanisms drive information diffuse in the networks? How does the structure and size of the cascades evolve in time? When and which users will adopt a certain message? Approaching these questions can considerably deepen our understanding about information cascades and facilitate various vital applications, including viral marketing, rumor prevention and even link prediction. Most previous works focus only on the final cascade size prediction. Meanwhile, they are always cascade graph dependent methods, which make them towards large cascades prediction and lead to the criticism that cascades may only be predictable after they have already grown large. In this paper, we study a fundamental problem: full-scale cascade dynamics prediction. That is, how to predict when and which users are activated at any time point of a cascading process. Here we propose a unified framework, FScaleCP, to solve the problem. Given history cascades, we first model the local spreading behaviors as a classification problem. Through data-driven learning, we recognize the common patterns by measuring the driving mechanisms of cascade dynamics. After that we present an intuitive asynchronous propagation method for full-scale cascade dynamics prediction by effectively aggregating the local spreading behaviors. Extensive experiments on social network data set suggest that the proposed method performs noticeably better than other state-of-the-art baselines.

Connected through hyperedge HE-1 (induced by source paper "predicting the evolution of complex networks via similarity dynamics"):
  with members:
    - "the lifecycle and cascade of wechat social messaging groups"
    - "ultra accurate collaborative information filtering via directed user similarity"
    - "can cascades be predicted"
    - "integrated structure investigation in complex networks by label propagation"
    - "the rich club phenomenon in the internet topology"

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)