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: "target set selection problem for honeycomb networks"
  Abstract: Let $G$ be a graph with a threshold function $\theta:V(G)\rightarrow \mathbb{N}$ such that $1\leq \theta(v)\leq d_G(v)$ for every vertex $v$ of $G$, where $d_G(v)$ is the degree of $v$ in $G$. Suppose we are given a target set $S\subseteq V(G)$. The paper considers the following repetitive process on $G$. At time step 0 the vertices of $S$ are colored black and the other vertices are colored white. After that, at each time step $t>0$, the colors of white vertices (if any) are updated according to the following rule. All white vertices $v$ that have at least $\theta(v)$ black neighbors at the time step $t-1$ are colored black, and the colors of the other vertices do not change. The process runs until no more white vertices can update colors from white to black. The following optimization problem is called Target Set Selection: Finding a target set $S$ of smallest possible size such that all vertices in $G$ are black at the end of the process. Such an $S$ is called an {\em optimal target set} for $G$ under the threshold function $\theta$. We are interested in finding an optimal target set for the well-known class of honeycomb networks under an important threshold function called {\em strict majority threshold}, where $\theta(v)=\lceil (d_G(v)+1)/2\rceil$ for each vertex $v$ in $G$. In a graph $G$, a {\em feedback vertex set} is a subset $S\subseteq V(G)$ such that the subgraph induced by $V(G)\setminus S$ is cycle-free. In this paper we give exact value on the size of the optimal target set for various kinds of honeycomb networks under strict majority threshold, and as a by-product we also provide a minimum feedback vertex set for different kinds regular graphs in the class of honeycomb networks

Connected through hyperedge HE-1 (induced by source paper "inapproximability and parameterized results for the target set selection problem"):
  with members:
    - "the role of social networks in information diffusion"

Connected through hyperedge HE-2 (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"
    - "influence diffusion in social networks under time window constraints"

Connected through hyperedge HE-3 (induced by source paper "an inapproximability result for the target set selection problem on bipartite graphs"):
  with members:
    - "target set selection in dense graph classes"

Connected through hyperedge HE-4 (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-5 (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"
    - "influence diffusion in social networks under time window constraints"
    - "contagious sets in expanders"

Connected through hyperedge HE-6 (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"
    - "influence diffusion in social networks under time window constraints"
    - "contagious sets in expanders"

Connected through hyperedge HE-7 (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"
    - "influence diffusion in social networks under time window constraints"
    - "contagious sets in expanders"

Connected through hyperedge HE-8 (induced by source paper "optimizing spread of influence in weighted social networks via partial incentives"):
  with members:
    - "contagious sets in dense graphs"
    - "latency bounded target set selection in social networks"
    - "contagious sets in expanders"

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.DM(Discrete Mathematics)