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 in dynamic social networks"
  Abstract: For the purpose of propagating information and ideas through a social network, a seeding strategy aims to find a small set of seed users that are able to maximize the spread of the influence, which is termed influence maximization problem. Despite a large number of works have studied this problem, the existing seeding strategies are limited to the models that cannot fully capture the characteristics of real-world social networks. In fact, due to high-speed data transmission and large population of participants, the diffusion processes in real-world social networks have many aspects of uncertainness. As shown in the experiments, when taking such uncertainness into account, the state-of-the-art seeding strategies are pessimistic as they fail to trace the influence diffusion. In this paper, we study the strategies that select seed users in an adaptive manner. We first formally model the dynamic independent Cascade model and introduce the concept of adaptive seeding strategy. Then, based on the proposed model, we show that a simple greedy adaptive seeding strategy finds an effective solution with a provable performance guarantee. Besides the greedy algorithm, an efficient heuristic algorithm is provided for better scalability. Extensive experiments have been performed on both the real-world networks and synthetic power-law networks. The results herein demonstrate the superiority of the adaptive seeding strategies to other baseline methods.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "computational aspects of optimal strategic network diffusion"):
  with members:
    - "balancing speed and coverage by sequential seeding in complex networks"
    - "scalable methods for adaptively seeding a social network"
    - "analytical approach to parallel repetition"
    - "a data based approach to social influence maximization"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "combined centrality measures for an improved characterization of influence spread in social networks"):
  with members:
    - "simulating sir processes on networks using weighted shortest paths"
    - "gemsec graph embedding with self clustering"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "effector detection in social networks"
    - "networkit a tool suite for large scale complex network analysis"

Connected through hyperedge HE-4 (induced by source paper "thompson sampling for combinatorial network optimization in unknown environments"):
  with members:
    - "batch size independent regret bounds for the combinatorial multi armed bandit problem"
    - "stochastic online learning with probabilistic graph feedback"
    - "combinatorial bandits with full bandit feedback sample complexity and regret minimization"
    - "thompson sampling for combinatorial semi bandits"
    - "bandits with side observations bounded vs logarithmic regret"
    - "analysis of thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "regret bounds for stochastic combinatorial multi armed bandits with linear space complexity"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"

Connected through hyperedge HE-5 (induced by source paper "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback"):
  with members:
    - "near optimal adaptivity gaps for stochastic multi value probing"
    - "adaptive influence maximization with myopic feedback"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "multi round influence maximization"
    - "adaptive submodular influence maximization with myopic feedback"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "boosting information spread an algorithmic approach"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"

Connected through hyperedge HE-6 (induced by source paper "social influence and radicalization a social data analytics study"):
  with members:
    - "data curation apis"
    - "least cost influence maximization across multiple social networks"
    - "failedrevolutions using twitter to study the antecedents of isis support"
    - "klout score measuring influence across multiple social networks"
    - "online social media in the syria conflict encompassing the extremes and the in betweens"
    - "big data and cross document coreference resolution current state and future opportunities"
    - "maximizing social influence in nearly optimal time"
    - "on budgeted influence maximization in social networks"

Connected through hyperedge HE-7 (induced by source paper "evolving influence maximization in evolving networks"):
  with members:
    - "time is what prevents everything from happening at once propagation time conscious influence maximization"
    - "outward influence and cascade size estimation in billion scale networks"
    - "online influence maximization in non stationary social networks"
    - "boosting information spread an algorithmic approach"
    - "online influence maximization"
    - "seed selection for spread of influence in social networks temporal vs static approach"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"

Connected through hyperedge HE-8 (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"
    - "adaptive influence maximization under general feedback models"
    - "multi round influence maximization"
    - "adaptive submodular influence maximization with myopic feedback"
    - "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"

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)