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 for conservative populations"
  Abstract: Let $G = (V,E)$ be a graph on $n$ vertices, where $d_v$ denotes the degree of vertex $v$, and $t_v$ is a threshold associated with $v$. We consider a process in which initially a set $S$ of vertices becomes active, and thereafter, in discrete time steps, every vertex $v$ that has at least $t_v$ active neighbors becomes active as well. The set $S$ is contagious if eventually all $V$ becomes active. The target set selection problem TSS asks for the smallest contagious set. TSS is NP-hard and moreover, notoriously difficult to approximate. #R##N#In the conservative special case of TSS, $t_v > \frac{1}{2}d_v$ for every $v \in V$. In this special case, TSS can be approximated within a ratio of $O(\Delta)$, where $\Delta = \max_{v \in V}[d_v]$. In this work we introduce a more general class of TSS instances that we refer to as conservative on average (CoA), that satisfy the condition $\sum_{v\in V} t_v > \frac{1}{2}\sum_{v \in V} d_v$. We design approximation algorithms for some subclasses of CoA. For example, if $t_v \geq \frac{1}{2}d_v$ for every $v \in V$, we can find in polynomial time a contagious set of size $\tilde{O}\left(\Delta \cdot OPT^2 \right)$, where $OPT$ is the size of a smallest contagious set in $G$. We also provide several hardness of approximation results. For example, assuming the unique games conjecture, we prove that TSS on CoA instances with $\Delta \le 3$ cannot be approximated within any constant factor. #R##N#We also present results concerning the fixed parameter tractability of CoA TSS instances, and approximation algorithms for a related problem, that of TSS with partial incentives.

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)