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: "spread of influence in weighted networks under time and budget constraints"
  Abstract: Given a network represented by a weighted directed graph G, we consider the problem of finding a bounded cost set of nodes S such that the influence spreading from S in G, within a given time bound, is as large as possible. The dynamic that governs the spread of influence is the following: initially only elements in S are influenced; subsequently at each round, the set of influenced elements is augmented by all nodes in the network that have a sufficiently large number of already influenced neighbors. We prove that the problem is NP-hard, even in simple networks like complete graphs and trees. We also derive a series of positive results. We present exact pseudo-polynomial time algorithms for general trees, that become polynomial time in case the trees are unweighted. This last result improves on previously published results. We also design polynomial time algorithms for general weighted paths and cycles, and for unweighted 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"
    - "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"
    - "discovering small target sets in social networks a fast and effective algorithm"
    - "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 "evangelism in social networks algorithms and complexity"):
  with members:
    - "target set selection in dense graph classes"
    - "on finding small sets that influence large networks"
    - "contagious sets in dense graphs"
    - "distance constrained labeling on graphs with bounded neighborhood diversity"
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "latency bounded target set selection in social networks"
    - "using neighborhood diversity to solve hard problems"
    - "algorithmic meta theorems"

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"
    - "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"
    - "influence diffusion in social networks under time window constraints"
    - "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:
    - "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"
    - "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.SI(Social and Information Networks)