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: "influence diffusion in social networks under time window constraints"
  Abstract: We study a combinatorial model of the spread of influence in networks that generalizes existing schemata recently proposed in the literature. In our model, agents change behaviors/opinions on the basis of information collected from their neighbors in a time interval of bounded size whereas agents are assumed to have unbounded memory in previously studied scenarios. In our mathematical framework, one is given a network $G=(V,E)$, an integer value $t(v)$ for each node $v\in V$, and a time window size $\lambda$. The goal is to determine a small set of nodes (target set) that influences the whole graph. The spread of influence proceeds in rounds as follows: initially all nodes in the target set are influenced; subsequently, in each round, any uninfluenced node $v$ becomes influenced if the number of its neighbors that have been influenced in the previous $\lambda$ rounds is greater than or equal to $t(v)$. We prove that the problem of finding a minimum cardinality target set that influences the whole network $G$ is hard to approximate within a polylogarithmic factor. On the positive side, we design exact polynomial time algorithms for paths, rings, trees, and complete 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"
    - "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"
    - "latency bounded target set selection in social networks"
    - "how to influence people with partial incentives"
    - "contagious sets in expanders"

Connected through hyperedge HE-2 (induced by source paper "a computational study of f reversible processes on graphs"):
  with members:
    - "the predecessor existence problem for k reversible processes"

Connected through hyperedge HE-3 (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"
    - "latency bounded target set selection in social networks"
    - "how to influence people with partial incentives"

Connected through hyperedge HE-4 (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"
    - "latency bounded target set selection in social networks"
    - "a scalable heuristic for viral marketing under the tipping model"
    - "contagious sets in expanders"
    - "target set selection problem for honeycomb networks"

Connected through hyperedge HE-5 (induced by source paper "on finding small sets that influence large networks"):
  with members:
    - "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"
    - "a scalable heuristic for viral marketing under the tipping model"
    - "contagious sets in expanders"
    - "target set selection problem for honeycomb networks"

Connected through hyperedge HE-6 (induced by source paper "spread of influence in weighted networks under time and budget constraints"):
  with members:
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "latency bounded target set selection in social networks"
    - "how to go viral cheaply and quickly"
    - "contagious sets in expanders"
    - "target set selection problem for honeycomb networks"

Connected through hyperedge HE-7 (induced by source paper "latency bounded target set selection in social networks"):
  with members:
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "on the approximability and exact algorithms for vector domination and related problems in graphs"
    - "contagious sets in expanders"
    - "minimum weight dynamo and fast opinion spreading"
    - "target set selection problem for honeycomb networks"

Connected through hyperedge HE-8 (induced by source paper "how to go viral cheaply and quickly"):
  with members:
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "latency bounded target set selection in social networks"
    - "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)