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: "influence maximization with bandits"
  Abstract: We consider the problem of \emph{influence maximization}, the problem of maximizing the number of people that become aware of a product by finding the `best' set of `seed' users to expose the product to. Most prior work on this topic assumes that we know the probability of each user influencing each other user, or we have data that lets us estimate these influences. However, this information is typically not initially available or is difficult to obtain. To avoid this assumption, we adopt a combinatorial multi-armed bandit paradigm that estimates the influence probabilities as we sequentially try different seed sets. We establish bounds on the performance of this procedure under the existing edge-level feedback as well as a novel and more realistic node-level feedback. Beyond our theoretical results, we describe a practical implementation and experimentally demonstrate its efficiency and effectiveness on four real datasets.

Connected through hyperedge HE-1 (induced by source paper "batch size independent regret bounds for the combinatorial multi armed bandit problem"):
  with members:
    - "thompson sampling for combinatorial semi bandits"
    - "thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "combinatorial multi armed bandit with general reward functions"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "unifying count based exploration and intrinsic motivation"
    - "combinatorial semi bandit with known covariance"
    - "combinatorial cascading bandits"

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"
    - "online influence maximization"
    - "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"
    - "online influence maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-4 (induced by source paper "stochastic online learning with probabilistic graph feedback"):
  with members:
    - "cleaning up the neighborhood a full classification for adversarial partial monitoring"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "information directed sampling for stochastic bandits with graph feedback"
    - "online learning with feedback graphs without the graphs"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"

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"
    - "online influence maximization"
    - "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 "multi round influence maximization"):
  with members:
    - "robust influence maximization"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "robust influence maximization"
    - "online influence maximization"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "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-7 (induced by source paper "how to maximize the spread of social influence a survey"):
  with members:
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "leveraging discarded samples for tighter estimation of multiple set aggregates"

Connected through hyperedge HE-8 (induced by source paper "online influence maximization with local observations"):
  with members:
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "a drifting games analysis for online learning and applications to boosting"
    - "a parameter free hedging algorithm"

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)