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: "online influence maximization"
  Abstract: Social networks are commonly used for marketing purposes. For example, free samples of a product can be given to a few influential social network users (or seed nodes), with the hope that they will convince their friends to buy it. One way to formalize this objective is through the problem of influence maximization (or IM), whose goal is to find the best seed nodes to activate under a fixed budget, so that the number of people who get influenced in the end is maximized. Solutions to IM rely on the influence probability that a user influences another one. However, this probability information may be unavailable or incomplete. In this paper, we study IM in the absence of complete information on influence probability. We call this problem Online Influence Maximization (OIM), since we learn influence probabilities at the same time we run influence campaigns. To solve OIM, we propose a multiple-trial approach, where (1) some seed nodes are selected based on existing influence information; (2) an influence campaign is started with these seed nodes; and (3) user feedback is used to update influence information. We adopt Explore-Exploit strategies, which can select seed nodes using either the current influence probability estimation (exploit), or the confidence bound on the estimation (explore). Any existing IM algorithm can be used in this framework. We also develop an incremental algorithm that can significantly reduce the overhead of handling user feedback information. Our experiments show that our solution is more effective than traditional IM methods on the partial information.

Connected through hyperedge HE-1 (induced by source paper "seeding with costly network information"):
  with members:
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "metric sublinear algorithms via linear sampling"
    - "the importance of communities for learning to influence"
    - "stochastic submodular maximization the case of coverage functions"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "robust influence maximization"
    - "almost optimal streaming algorithms for coverage problems"
    - "scalable methods for adaptively seeding a social network"

Connected through hyperedge HE-2 (induced by source paper "automatic ensemble learning for online influence maximization"):
  with members:
    - "thompson sampling for stochastic bandits with graph feedback"
    - "model independent online learning for influence maximization"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "influence maximization with bandits"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "an information theoretic analysis of thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-3 (induced by source paper "factorization bandits for online influence maximization"):
  with members:
    - "online influence maximization with local observations"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "model independent online learning for influence maximization"
    - "node2vec scalable feature learning for networks"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization with bandits"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-4 (induced by source paper "evolving influence maximization in evolving networks"):
  with members:
    - "adaptive influence maximization in dynamic social networks"
    - "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"
    - "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-5 (induced by source paper "online learning and optimization under a new linear threshold model with negative influence"):
  with members:
    - "holistic influence maximization combining scalability and efficiency with opinion aware models"
    - "node2vec scalable feature learning for networks"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "influence maximization with bandits"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"

Connected through hyperedge HE-6 (induced by source paper "tracking influential nodes in time decaying dynamic interaction networks"):
  with members:
    - "real time influence maximization on dynamic social streams"
    - "tracking influential individuals in dynamic networks"
    - "submodular optimization over sliding windows"
    - "robust influence maximization"
    - "boosting information spread an algorithmic approach"
    - "submodular maximization over sliding windows"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "the bursty dynamics of the twitter information network"

Connected through hyperedge HE-7 (induced by source paper "multi round influence maximization"):
  with members:
    - "robust influence maximization"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "robust influence maximization"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization with bandits"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-8 (induced by source paper "algorithms for online influencer marketing"):
  with members:
    - "robust influence maximization"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "robust influence maximization"
    - "distinguishing between topical and non topical information diffusion mechanisms in social media"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "optimal discovery with probabilistic expert advice finite time analysis and macroscopic optimality"

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)