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: "beyond worst case in approximability of nonsubmodular influence maximization"
  Abstract: We consider the problem of maximizing the spread of influence in a social network by choosing a fixed number of initial seeds, formally referred to as the influence maximization problem. It admits a (1−1/e)-factor approximation algorithm if the influence function is submodular. Otherwise, in the worst case, the problem is NP-hard to approximate to within a factor of N1−v. This article studies whether this worst-case hardness result can be circumvented by making assumptions about either the underlying network topology or the cascade model. All our assumptions are motivated by many real-life social network cascades.#R##N##R##N#First, we present strong inapproximability results for a very restricted class of networks called the (stochastic) hierarchical blockmodel, a special case of the well-studied (stochastic) blockmodel in which relationships between blocks admit a tree structure. We also provide a dynamic-programming-based polynomial time algorithm, which optimally computes a directed variant of the influence maximization problem on hierarchical blockmodel networks. Our algorithm indicates that the inapproximability result is due to the bidirectionality of influence between agent-blocks.#R##N##R##N#Second, we present strong inapproximability results for a class of influence functions that are “almost” submodular, called 2-quasi-submodular. Our inapproximability results hold even for any 2-quasi-submodular f fixed in advance. This result also indicates that the “threshold” between submodularity and nonsubmodularity is sharp, regarding the approximability of influence maximization.

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"
    - "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"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-2 (induced by source paper "seeding with costly network information"):
  with members:
    - "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"
    - "scalable methods for adaptively seeding a social network"

Connected through hyperedge HE-3 (induced by source paper "maximizing welfare in social networks under a utility driven influence diffusion model"):
  with members:
    - "an issue in the martingale analysis of the influence maximization algorithm imm"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "inferring networks of substitutable and complementary products"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"

Connected through hyperedge HE-4 (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"
    - "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"
    - "maximizing social influence in nearly optimal time"
    - "the diffusion of networking technologies"

Connected through hyperedge HE-5 (induced by source paper "don t be greedy leveraging community structure to find high quality seed sets for influence maximization"):
  with members:
    - "discovering small target sets in social networks a fast and effective algorithm"
    - "a cost function for similarity based hierarchical clustering"
    - "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"
    - "competitive contagion in networks"
    - "finding overlapping communities in social networks toward a rigorous approach"
    - "information contagion an empirical study of the spread of news on digg and twitter 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)