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: "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback"
  Abstract: In this paper, we study the adaptivity gap of the influence maximization problem under independent cascade model when full-adoption feedback is available. Our main results are to derive upper bounds on several families of well-studied influence graphs, including in-arborescences, out-arborescences and bipartite graphs. Especially, we prove that the adaptivity gap for the in-arborescence is between $[\frac{e}{e-1}, \frac{2e}{e - 1}]$ and for the out-arborescence, the gap is between $[\frac{e}{e-1}, 2]$. These are the first constant upper bounds in the full-adoption feedback model. We provide several novel ideas to tackle with correlated feedback appearing in the adaptive stochastic optimization, which we believe to be of independent interests.

Connected through hyperedge HE-1 (induced by source paper "time constrained adaptive influence maximization"):
  with members:
    - "adaptive influence maximization with myopic feedback"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "adaptive influence maximization under general feedback models"
    - "on misinformation containment in online social networks"
    - "multi round influence maximization"
    - "adaptive submodular influence maximization with myopic feedback"
    - "fake news mitigation via point process based intervention"
    - "adaptive influence maximization in dynamic social networks"

Connected through hyperedge HE-2 (induced by source paper "limitations of greed influence maximization in undirected networks re visited"):
  with members:
    - "adaptive greedy versus non adaptive greedy for influence maximization"
    - "adaptive influence maximization with myopic feedback"
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "don t be greedy leveraging community structure to find high quality seed sets for influence maximization"
    - "holistic influence maximization combining scalability and efficiency with opinion aware models"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "staticgreedy solving the scalability accuracy dilemma in influence maximization"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-3 (induced by source paper "adaptive greedy versus non adaptive greedy for influence maximization"):
  with members:
    - "adaptive influence maximization with myopic feedback"
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "don t be greedy leveraging community structure to find high quality seed sets for influence maximization"
    - "holistic influence maximization combining scalability and efficiency with opinion aware models"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "staticgreedy solving the scalability accuracy dilemma in influence maximization"
    - "maximizing social influence in nearly optimal time"
    - "the diffusion of networking technologies"

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)