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: "irie scalable and robust influence maximization in social networks"
  Abstract: Influence maximization is the problem of selecting top $k$ seed nodes in a social network to maximize their influence coverage under certain influence diffusion models. In this paper, we propose a novel algorithm IRIE that integrates a new message passing based influence ranking (IR), and influence estimation (IE) methods for influence maximization in both the independent cascade (IC) model and its extension IC-N that incorporates negative opinion propagations. Through extensive experiments, we demonstrate that IRIE matches the influence coverage of other algorithms while scales much better than all other algorithms. Moreover IRIE is more robust and stable than other algorithms both in running time and memory usage for various density of networks and cascade size. It runs up to two orders of magnitude faster than other state-of-the-art algorithms such as PMIA for large networks with tens of millions of nodes and edges, while using only a fraction of memory comparing with PMIA.

Connected through hyperedge HE-1 (induced by source paper "limitations of greed influence maximization in undirected networks re visited"):
  with members:
    - "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback"
    - "adaptive greedy versus non adaptive greedy for influence maximization"
    - "adaptive influence maximization with myopic feedback"
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "don t be greedy leveraging community structure to find high quality seed sets for influence maximization"
    - "holistic influence maximization combining scalability and efficiency with opinion aware models"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "staticgreedy solving the scalability accuracy dilemma in influence maximization"

Connected through hyperedge HE-2 (induced by source paper "rcelf a residual based approach for influence maximization problem"):
  with members:
    - "importance sketching of influence dynamics in billion scale networks"
    - "influence maximization meets efficiency and effectiveness a hop based approach"
    - "holistic influence maximization combining scalability and efficiency with opinion aware models"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "imrank influence maximization via finding self consistent ranking"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "staticgreedy solving the scalability accuracy dilemma in influence maximization"

Connected through hyperedge HE-3 (induced by source paper "influence maximization in unknown social networks learning policies for effective graph sampling"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "learning structural node embeddings via diffusion wavelets"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "prioritized experience replay"
    - "deepwalk online learning of social representations"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-4 (induced by source paper "monstor an inductive approach for estimating and maximizing influence over unseen social networks"):
  with members:
    - "disco influence maximization meets network embedding and deep learning"
    - "large scale learnable graph convolutional networks"

Connected through hyperedge HE-5 (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"
    - "online influence maximization"

Connected through hyperedge HE-6 (induced by source paper "efficient approximation algorithms for adaptive seed minimization"):
  with members:
    - "cost effective seed selection in online social networks"
    - "influence maximization meets efficiency and effectiveness a hop based approach"
    - "adaptive influence maximization in social networks why commit when you can adapt"
    - "holistic influence maximization combining scalability and efficiency with opinion aware models"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "scalable methods for adaptively seeding a social network"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "imrank influence maximization via finding self consistent ranking"

Connected through hyperedge HE-7 (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 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"

Connected through hyperedge HE-8 (induced by source paper "scalable influence estimation without sampling"):
  with members:
    - "cascade size distributions and why they matter"
    - "outward influence and cascade size estimation in billion scale networks"
    - "nonbacktracking bounds on the influence in independent cascade models"
    - "optimal deployment of resources for maximizing impact in spreading processes"
    - "robust influence maximization"
    - "reconstructing parameters of spreading models from partial observations"
    - "robust influence maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"

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)