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: "no time to observe adaptive influence maximization with partial feedback"
  Abstract: Although influence maximization problem has been extensively studied over the past ten years, majority of existing work adopt one of the following models: \emph{full-feedback model} or \emph{zero-feedback model}. In the zero-feedback model, we have to commit the seed users all at once in advance, this strategy is also known as non-adaptive policy. In the full-feedback model, we select one seed at a time and wait until the diffusion completes, before selecting the next seed. Full-feedback model has better performance but potentially huge delay, zero-feedback model has zero delay but poorer performance since it does not utilize the observation that may be made during the seeding process. To fill the gap between these two models, we propose \emph{Partial-feedback Model}, which allows us to select a seed at any intermediate stage. We develop a novel $\alpha$-greedy policy that, for the first time, achieves a bounded approximation ratio.

Connected through hyperedge HE-1 (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 influence maximization under general feedback models"
    - "adaptive submodular influence maximization with myopic feedback"
    - "robust influence maximization"
    - "profit maximization over social networks"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "pricing strategies for viral marketing on social networks"

Connected through hyperedge HE-2 (induced by source paper "price of dependence stochastic submodular maximization with dependent items"):
  with members:
    - "near optimal adaptivity gaps for stochastic multi value probing"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "discrete stochastic submodular maximization adaptive vs non adaptive vs offline"
    - "submodular stochastic probing on matroids"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "a two phase investment game for competitive opinion dynamics in social networks"):
  with members:
    - "an integrated framework for competitive multi channel marketing of multi featured products"
    - "multi round influence maximization"
    - "effectiveness of diffusing information through a social network in multiple phases"
    - "optimal multiphase investment strategies for influencing opinions in a social network"
    - "adaptive influence maximization in dynamic social networks"
    - "optimal investment strategies for competing camps in a social network a broad framework"
    - "opinion dynamics in social networks with hostile camps consensus vs polarization"
    - "information diffusion in social networks in two phases"

Connected through hyperedge HE-4 (induced by source paper "optimal multiphase investment strategies for influencing opinions in a social network"):
  with members:
    - "effectiveness of diffusing information through a social network in multiple phases"
    - "adaptive influence maximization in dynamic social networks"
    - "optimal investment strategies for competing camps in a social network a broad framework"
    - "strategic disclosure of opinions on a social network"
    - "information diffusion in social networks in two phases"
    - "a multi phase approach for improving information diffusion in social networks"
    - "scalable methods for adaptively seeding a social network"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"

Connected through hyperedge HE-5 (induced by source paper "stochastic coupon probing in social networks"):
  with members:
    - "optimizing ad allocation in social advertising"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "submodular stochastic probing on matroids"
    - "a stochastic probing problem with applications"
    - "maximizing social influence in nearly optimal time"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "effectiveness of diffusing information through a social network in multiple phases"):
  with members:
    - "optimal multiphase investment strategies for influencing opinions in a social network"
    - "adaptive influence maximization in dynamic social networks"
    - "information diffusion in social networks in two phases"
    - "scalable methods for adaptively seeding a social network"
    - "irie scalable and robust influence maximization in social networks"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "adaptive submodular influence maximization with myopic feedback"):
  with members:
    - "multi round influence maximization extended version"
    - "adaptive influence maximization in social networks why commit when you can adapt"
    - "robust influence maximization"
    - "going viral optimizing discount allocation in social networks for influence maximization"
    - "maximizing social influence in nearly optimal time"
    - "a data based approach to social influence maximization"
    - "irie scalable and robust influence maximization in social networks"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)