human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cascade size distributions and why they matter"):
  Abstract: How likely is it that a few initial node activations are amplified to produce large response cascades that span a considerable part of an entire network? Our answer to this question relies on the Independent Cascade Model for weighted directed networks. In using this model, most of our insights have been derived from the study of average effects. Here, we shift the focus on the full probability distribution of the final cascade size. This shift allows us to explore both typical cascade outcomes and improbable but relevant extreme events. We present an efficient message passing algorithm to compute the final cascade size distribution and activation probabilities of nodes conditional on the final cascade size. Our approach is exact on trees but can be applied to any network topology. It approximates locally tree-like networks well and can lead to surprisingly good performance on more dense networks, as we show using real world data, including a miRNA- miRNA probabilistic interaction network for gastrointestinal cancer. We demonstrate the utility of our algorithms for clustering of nodes according to their functionality and influence maximization.

Includes members:
  Member "fake news detection in social networks via crowd signals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attributed multi relational attention network for fact checking url recommendation")
  Member "balancing information exposure in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combating fake news a survey on identification and mitigation techniques")
  Member "mitigating overexposure in viral marketing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "opinion dynamics with varying susceptibility to persuasion")
  Member "catch em all locating multiple diffusion sources in networks with partial observations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "information source detection with limited time knowledge")
  Member "optimal deployment of resources for maximizing impact in spreading processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable influence estimation without sampling")
  Member "online influence maximization under independent cascade model with semi bandit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "seeding with costly network information")
  Member "back to the past source identification in diffusion networks from partially observed cascades":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "necessary and sufficient budgets in information source finding with querying adaptivity gap")

Question: Please tell me which class the source paper 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.AI(Artificial Intelligence)