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: "maximizing social influence in nearly optimal time"
  Abstract: Diffusion is a fundamental graph process, underpinning such phenomena as epidemic disease contagion and the spread of innovation by word-of-mouth. We address the algorithmic problem of finding a set of k initial seed nodes in a network so that the expected size of the resulting cascade is maximized, under the standard independent cascade model of network diffusion. Runtime is a primary consideration for this problem due to the massive size of the relevant input networks. #R##N#We provide a fast algorithm for the influence maximization problem, obtaining the near-optimal approximation factor of (1 - 1/e - epsilon), for any epsilon > 0, in time O((m+n)k log(n) / epsilon^2). Our algorithm is runtime-optimal (up to a logarithmic factor) and substantially improves upon the previously best-known algorithms which run in time Omega(mnk POLY(1/epsilon)). Furthermore, our algorithm can be modified to allow early termination: if it is terminated after O(beta(m+n)k log(n)) steps for some beta < 1 (which can depend on n), then it returns a solution with approximation factor O(beta). Finally, we show that this runtime is optimal (up to logarithmic factors) for any beta and fixed seed size k.

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 "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-3 (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-4 (induced by source paper "maximizing influence based group shapley centrality"):
  with members:
    - "game theoretic network centrality a review"
    - "computing top k closeness centrality faster in unweighted graphs"
    - "scaling up group closeness maximization"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "axioms for centrality"
    - "efficient computation of the shapley value for game theoretic network centrality"

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 "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback"):
  with members:
    - "near optimal adaptivity gaps for stochastic multi value probing"
    - "adaptive influence maximization with myopic feedback"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "multi round influence maximization"
    - "adaptive submodular influence maximization with myopic feedback"
    - "adaptive influence maximization in dynamic social networks"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "boosting information spread an algorithmic approach"

Connected through hyperedge HE-8 (induced by source paper "disco influence maximization meets network embedding and deep learning"):
  with members:
    - "multi round influence maximization"
    - "deepinf social influence prediction with deep learning"
    - "co domain embedding using deep quadruplet networks for unseen traffic sign recognition"
    - "model independent online learning for influence maximization"
    - "learning combinatorial optimization algorithms over graphs"
    - "node2vec scalable feature learning for networks"
    - "discriminative embeddings of latent variable models for structured data"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale 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.DS(Data Structures and Algorithms)