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: "diffusion aware sampling and estimation in information diffusion networks"
  Abstract: Partially-observed data collected by sampling methods is often being studied to obtain the characteristics of information diffusion networks. However, these methods usually do not consider the behavior of diffusion process. In this paper, we propose a novel two-step (sampling/estimation) measurement framework by utilizing the diffusion process characteristics. To this end, we propose a link-tracing based sampling design which uses the infection times as local information without any knowledge about the latent structure of diffusion network. To correct the bias of sampled data, we introduce three estimators for different categories, link-based, node-based, and cascade-based. To the best of our knowledge, this is the first attempt to introduce a complete measurement framework for diffusion networks. We also show that the estimator plays an important role in correcting the bias of sampling from diffusion networks. Our comprehensive empirical analysis over large synthetic and real datasets demonstrates that in average, the proposed framework outperforms the common BFS and RW sampling methods in terms of link-based characteristics by about 37% and 35%, respectively.

Connected through hyperedge HE-1 (induced by source paper "spreading processes in multilayer networks"):
  with members:
    - "clustering attributed graphs models measures and methods"
    - "multidimensional epidemic thresholds in diffusion processes over interdependent networks"
    - "multiple routes transmitted epidemics on multiplex networks"
    - "measuring large scale social networks with high resolution"
    - "social bootstrapping how pinterest and last fm social communities benefit by borrowing links from facebook"
    - "diffusion of innovations over multiplex social networks"
    - "cooperation optimized design for information dissemination in vehicular networks using evolutionary game theory"
    - "exploiting cellular data for disease containment and information campaigns strategies in country wide epidemics"

Connected through hyperedge HE-2 (induced by source paper "a measurement framework for directed networks"):
  with members:
    - "sampling from diffusion networks"
    - "towards unbiased bfs sampling"
    - "multigraph sampling of online social networks"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-3 (induced by source paper "sampling from diffusion networks"):
  with members:
    - "inferring networks of diffusion and influence"

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)