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: "discovering small target sets in social networks a fast and effective algorithm"
  Abstract: Given a network represented by a graph $G=(V,E)$, we consider a dynamical process of influence diffusion in $G$ that evolves as follows: Initially only the nodes of a given $S\subseteq V$ are influenced; subsequently, at each round, the set of influenced nodes is augmented by all the nodes in the network that have a sufficiently large number of already influenced neighbors. The question is to determine a small subset of nodes $S$ (\emph{a target set}) that can influence the whole network. This is a widely studied problem that abstracts many phenomena in the social, economic, biological, and physical sciences. It is known that the above optimization problem is hard to approximate within a factor of $2^{\log^{1-\epsilon}|V|}$, for any $\epsilon >0$. In this paper, we present a fast and surprisingly simple algorithm that exhibits the following features: 1) when applied to trees, cycles, or complete graphs, it always produces an optimal solution (i.e, a minimum size target set); 2) when applied to arbitrary networks, it always produces a solution of cardinality which improves on the previously known upper bound; 3) when applied to real-life networks, it always produces solutions that substantially outperform the ones obtained by previously published algorithms (for which no proof of optimality or performance guarantee is known in any class of graphs).

Connected through hyperedge HE-1 (induced by source paper "time bounded influence diffusion with incentives"):
  with members:
    - "selecting nodes and buying links to maximize the information diffusion in a network"
    - "evangelism in social networks algorithms and complexity"
    - "on finding small sets that influence large networks"
    - "spread of influence in weighted networks under time and budget constraints"
    - "latency bounded target set selection in social networks"
    - "how to influence people with partial incentives"
    - "influence diffusion in social networks under time window constraints"
    - "contagious sets in expanders"

Connected through hyperedge HE-2 (induced by source paper "whom to befriend to influence people"):
  with members:
    - "selecting nodes and buying links to maximize the information diffusion in a network"
    - "recommending links through influence maximization"
    - "spread of influence in weighted networks under time and budget constraints"
    - "contagious sets in dense graphs"
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "latency bounded target set selection in social networks"
    - "how to influence people with partial incentives"
    - "analytical approach to parallel repetition"

Connected through hyperedge HE-3 (induced by source paper "don t be greedy leveraging community structure to find high quality seed sets for influence maximization"):
  with members:
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "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.DS(Data Structures and Algorithms)