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: "adaptive influence maximization under general feedback models"
  Abstract: This note provides some analysis for the adaptive influence maximization problem under general Feedback Models with or without a time constraint.

Connected through hyperedge HE-1 (induced by source paper "time constrained adaptive influence maximization"):
  with members:
    - "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback"
    - "adaptive influence maximization with myopic feedback"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "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 "adaptive influence maximization with myopic feedback"):
  with members:
    - "near optimal adaptivity gaps for stochastic multi value probing"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "multi round influence maximization"
    - "adaptive submodular influence maximization with myopic feedback"
    - "adaptive influence maximization in dynamic social networks"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "a k hop collaborate game model adaptive strategy to maximize total revenue"):
  with members:
    - "adaptive influence maximization with myopic feedback"
    - "adaptive submodular influence maximization with myopic feedback"
    - "robust influence maximization"
    - "no time to observe adaptive influence maximization with partial feedback"
    - "profit maximization over social networks"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "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)