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: "on budgeted influence maximization in social networks"
  Abstract: Given a budget and arbitrary cost for selecting each node, the budgeted influence maximization (BIM) problem concerns selecting a set of seed nodes to disseminate some information that maximizes the total number of nodes influenced (termed as influence spread) in social networks at a total cost no more than the budget. Our proposed seed selection algorithm for the BIM problem guarantees an approximation ratio of (1 - 1/sqrt(e)). The seed selection algorithm needs to calculate the influence spread of candidate seed sets, which is known to be #P-complex. Identifying the linkage between the computation of marginal probabilities in Bayesian networks and the influence spread, we devise efficient heuristic algorithms for the latter problem. Experiments using both large-scale social networks and synthetically generated networks demonstrate superior performance of the proposed algorithm with moderate computation costs. Moreover, synthetic datasets allow us to vary the network parameters and gain important insights on the impact of graph structures on the performance of different algorithms.

Connected through hyperedge HE-1 (induced by source paper "election manipulation on social networks seeding edge removal edge addition"):
  with members:
    - "election control in social networks via edge addition or removal"
    - "controlling elections through social influence"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "minority becomes majority in social networks"
    - "maximizing the spread of cascades using network design"

Connected through hyperedge HE-2 (induced by source paper "social influence and radicalization a social data analytics study"):
  with members:
    - "adaptive influence maximization in dynamic social networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "election manipulation on social networks with messages on multiple candidates"):
  with members:
    - "controlling elections through social influence"
    - "minority becomes majority in social networks"

Connected through hyperedge HE-4 (induced by source paper "a survey on influence maximization in a social network"):
  with members:
    - "modeling information diffusion in social networks"
    - "information diffusion in social networks in two phases"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "latency bounded target set selection in social networks"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"

Connected through hyperedge HE-5 (induced by source paper "why approximate when you can get the exact optimal targeted viral marketing at scale"):
  with members:
    - "least cost influence maximization across multiple social networks"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "tiptop exact solutions for influence maximization in billion scale networks"):
  with members:
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "revenue maximization in incentivized social advertising"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "how to influence people with partial incentives"
    - "viral marketing meets social advertising ad allocation with minimum regret"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "maximizing social influence in nearly optimal time"
    - "social influence in social advertising evidence from field experiments"

Connected through hyperedge HE-8 (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"
    - "time critical influence maximization in social networks with time delayed diffusion process"
    - "irie scalable and robust influence maximization in social networks"
    - "on the number of iterations for dantzig wolfe optimization and packing covering approximation algorithms"

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)