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: "staticgreedy solving the scalability accuracy dilemma in influence maximization"
  Abstract: Influence maximization, defined as a problem of finding a set of seed nodes to trigger a maximized spread of influence, is crucial to viral marketing on social networks. For practical viral marketing on large scale social networks, it is required that influence maximization algorithms should have both guaranteed accuracy and high scalability. However, existing algorithms suffer a scalability-accuracy dilemma: conventional greedy algorithms guarantee the accuracy with expensive computation, while the scalable heuristic algorithms suffer from unstable accuracy   In this paper, we focus on solving this scalability-accuracy dilemma. We point out that the essential reason of the dilemma is the surprising fact that the submodularity, a key requirement of the objective function for a greedy algorithm to approximate the optimum, is not guaranteed in all conventional greedy algorithms in the literature of influence maximization. Therefore a greedy algorithm has to afford a huge number of Monte Carlo simulations to reduce the pain caused by unguaranteed submodularity. Motivated by this critical finding, we propose a static greedy algorithm, named StaticGreedy, to strictly guarantee the submodularity of influence spread function during the seed selection process. The proposed algorithm makes the computational expense dramatically reduced by two orders of magnitude without loss of accuracy. Moreover, we propose a dynamical update strategy which can speed up the StaticGreedy algorithm by 2-7 times on large scale social networks.

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"
    - "maximizing social influence in nearly optimal time"

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"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-3 (induced by source paper "adaptive greedy versus non adaptive greedy for 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 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"
    - "maximizing social influence in nearly optimal time"
    - "the diffusion of networking technologies"

Connected through hyperedge HE-4 (induced by source paper "top k influential nodes in social networks a game perspective"):
  with members:
    - "imrank influence maximization via finding self consistent ranking"

Connected through hyperedge HE-5 (induced by source paper "exact computation of influence spread by binary decision diagrams"):
  with members:
    - "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 "learning sentimental influences from users behaviors"):
  with members:
    - "modeling information propagation with survival theory"
    - "opinion maximization in social networks"
    - "learning user specific latent influence and susceptibility from information cascades"
    - "distributed representations of words and phrases and their compositionality"
    - "adadelta an adaptive learning rate method"
    - "structure and dynamics of information pathways in online media"
    - "how bad is forming your own opinion"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-7 (induced by source paper "holistic influence maximization combining scalability and efficiency with opinion aware models"):
  with members:
    - "imrank influence maximization via finding self consistent ranking"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "twitter opinion topic model extracting product opinions from tweets by leveraging hashtags and sentiment lexicon"
    - "viral marketing meets social advertising ad allocation with minimum regret"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"
    - "irie scalable and robust influence maximization in social networks"

Connected through hyperedge HE-8 (induced by source paper "temporal scaling in information propagation"):
  with members:
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "predicting adoption probabilities in social networks"
    - "quantifying long term scientific impact"
    - "structure and dynamics of information pathways in online media"
    - "uncovering the temporal dynamics of diffusion networks"
    - "inferring networks of diffusion and influence"
    - "using a model of social dynamics to predict popularity of news"
    - "novelty and collective attention"

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)