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: "time critical influence maximization in social networks with time delayed diffusion process"
  Abstract: Influence maximization is a problem of finding a small set of highly influential users, also known as seeds, in a social network such that the spread of influence under certain propagation models is maximized. In this paper, we consider time-critical influence maximization, in which one wants to maximize influence spread within a given deadline. Since timing is considered in the optimization, we also extend the Independent Cascade (IC) model and the Linear Threshold (LT) model to incorporate the time delay aspect of influence diffusion among individuals in social networks. We show that time-critical influence maximization under the time-delayed IC and LT models maintains desired properties such as submodularity, which allows a greedy approximation algorithm to achieve an approximation ratio of $1-1/e$. To overcome the inefficiency of the greedy algorithm, we design two heuristic algorithms: the first one is based on a dynamic programming procedure that computes exact influence in tree structures and directed acyclic subgraphs, while the second one converts the problem to one in the original models and then applies existing fast heuristic algorithms to it. Our simulation results demonstrate that our algorithms achieve the same level of influence spread as the greedy algorithm while running a few orders of magnitude faster, and they also outperform existing fast heuristics that disregard the deadline constraint and delays in diffusion.

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 "on the fairness of time critical influence maximization in social networks"):
  with members:
    - "group fairness in influence maximization"
    - "learning optimal and fair decision trees for non discriminative decision making"
    - "gaps in information access in social networks"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "fair allocation of indivisible public goods"
    - "approximate maximin shares for groups of agents"
    - "multiwinner elections with diversity constraints"

Connected through hyperedge HE-3 (induced by source paper "towards profit maximization for online social network providers"):
  with members:
    - "influence maximization meets efficiency and effectiveness a hop based approach"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "profit maximization over social networks"
    - "maximizing social influence in nearly optimal time"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "uncovering the temporal dynamics of diffusion networks"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-4 (induced by source paper "influence maximization meets efficiency and effectiveness a hop based approach"):
  with members:
    - "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"
    - "maximizing social influence in nearly optimal time"
    - "irie scalable and robust influence maximization in social networks"

Connected through hyperedge HE-5 (induced by source paper "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"):
  with members:
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "on budgeted influence maximization in social networks"
    - "irie scalable and robust influence maximization in social networks"
    - "on the number of iterations for dantzig wolfe optimization and packing covering approximation algorithms"

Connected through hyperedge HE-6 (induced by source paper "information diffusion in social networks in two phases"):
  with members:
    - "a multi phase approach for improving information diffusion in social networks"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "sketch based influence maximization and computation scaling up with guarantees"):
  with members:
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "trace complexity of network inference"
    - "scalable influence estimation in continuous time diffusion networks"
    - "maximizing social influence in nearly optimal time"
    - "uncovering the temporal dynamics of diffusion networks"
    - "irie scalable and robust influence maximization in social networks"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-8 (induced by source paper "influence maximization near optimal time complexity meets practical efficiency"):
  with members:
    - "maximizing social influence in nearly optimal time"
    - "a data based approach to social influence maximization"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"
    - "irie scalable and robust influence maximization in social networks"

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)