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: "latency bounded target set selection in social networks"
  Abstract: We study variants of the Target Set Selection problem, first proposed by Kempe et al. In our scenario one is given a graph G = (V,E), integer values t(v) for each vertex v, and the objective is to determine a small set of vertices (target set) that activates a given number (or a given subset) of vertices of G within a prescribed number of rounds. The activation process in G proceeds as follows: initially, at round 0, all vertices in the target set are activated; subsequently at each round r ≥ 1 every vertex of G becomes activated if at least t(v) of its neighbors are active by round r − 1. It is known that the problem of finding a minimum cardinality Target Set that eventually activates the whole graph G is hard to approximate to a factor better than \(O(2^{\log^{1-\epsilon }|V|})\). In this paper we give exact polynomial time algorithms to find minimum cardinality Target Sets in graphs of bounded clique-width, and exact linear time algorithms for trees.

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"
    - "discovering small target sets in social networks a fast and effective algorithm"
    - "on finding small sets that influence large networks"
    - "spread of influence in weighted networks under time and budget constraints"
    - "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 "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"
    - "on budgeted influence maximization in social networks"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"

Connected through hyperedge HE-3 (induced by source paper "new algorithms for weighted k domination and total k domination problems in proper interval graphs"):
  with members:
    - "complexity results for k domination and α domination problems and their variants"
    - "a width parameter useful for chordal and co comparability graphs"
    - "the parameterized complexity of domination type problems and application to linear codes"
    - "total vector domination for graphs with bounded branchwidth"
    - "on the approximability and exact algorithms for vector domination and related problems in graphs"

Connected through hyperedge HE-4 (induced by source paper "target set selection parameterized by clique width and maximum threshold"):
  with members:
    - "target set selection in dense graph classes"
    - "parameterized inapproximability of target set selection and generalizations"
    - "parameterized algorithms for modular width"
    - "confronting intractability via parameters"

Connected through hyperedge HE-5 (induced by source paper "complexity results for k domination and α domination problems and their variants"):
  with members:
    - "total vector domination for graphs with bounded branchwidth"
    - "on the approximability and exact algorithms for vector domination and related problems in graphs"
    - "analytical approach to parallel repetition"

Connected through hyperedge HE-6 (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"
    - "discovering small target sets in social networks a fast and effective algorithm"
    - "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"
    - "how to influence people with partial incentives"
    - "analytical approach to parallel repetition"

Connected through hyperedge HE-7 (induced by source paper "evangelism in social networks algorithms and complexity"):
  with members:
    - "target set selection in dense graph classes"
    - "on finding small sets that influence large networks"
    - "spread of influence in weighted networks under time and budget constraints"
    - "contagious sets in dense graphs"
    - "distance constrained labeling on graphs with bounded neighborhood diversity"
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "using neighborhood diversity to solve hard problems"
    - "algorithmic meta theorems"

Connected through hyperedge HE-8 (induced by source paper "discovering small target sets in social networks a fast and effective algorithm"):
  with members:
    - "on finding small sets that influence large networks"
    - "spread of influence in weighted networks under time and budget constraints"
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "a scalable heuristic for viral marketing under the tipping model"
    - "influence diffusion in social networks under time window constraints"
    - "contagious sets in expanders"
    - "target set selection problem for honeycomb 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)