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: "contagious sets in expanders"
  Abstract: We consider the following activation process in undirected graphs: a vertex is active either if it belongs to a set of initially activated vertices or if at some point it has at least $r$ active neighbors, where $r>1$ is the activation threshold. #R##N#A \emph{contagious set} is a set whose activation results with the entire graph being active. Given a graph $G$, let $m(G,r)$ be the minimal size of a contagious set. Computing $m(G,r)$ is NP-hard. #R##N#It is known that for every $d$-regular or nearly $d$-regular graph on $n$ vertices, $m(G,r) \le O(\frac{nr}{d})$. We consider such graphs that additionally have expansion properties, parameterized by the spectral gap and/or the girth of the graphs. #R##N#The general flavor of our results is that sufficiently strong expansion (e.g., $\lambda(G)=O(\sqrt{d})$, or girth $\Omega(\log \log d)$) implies that $m(G,2) \le O(\frac{n}{d^2})$ (and more generally, $m(G,r) \le O(\frac{n}{d^{r/(r-1)}})$). Significantly weaker expansion properties suffice in order to imply that $m(G,2)\le O(\frac{n \log d}{d^2})$. For example, we show this for graphs of girth at least~7, and for graphs with $\lambda(G)  \log n$. For such graphs we prove that $\Omega(\frac{n}{d^2 \log d}) \le m(G,2) \le O(\frac{n\log\log d}{d^2\log d})$ almost surely. #R##N#Our results are algorithmic, entailing simple and efficient algorithms for selecting contagious sets.

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"
    - "influence diffusion in social networks under time window constraints"

Connected through hyperedge HE-2 (induced by source paper "tight bounds on the minimum size of a dynamic monopoly"):
  with members:
    - "dynamic monopolies in reversible bootstrap percolation"
    - "opinion forming in erdos renyi random graph and expanders"
    - "biased majority rule cellular automata"
    - "majority model on random regular graphs"
    - "contagious sets in dense graphs"

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"
    - "influence diffusion in social networks under time window constraints"
    - "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"
    - "influence diffusion in social networks under time window constraints"
    - "target set selection problem for honeycomb networks"

Connected through hyperedge HE-6 (induced by source paper "bounds on the parameters of locally recoverable codes"):
  with members:
    - "cooperative local repair in distributed storage"
    - "cyclic lrc codes and their subfield subcodes"
    - "achieving arbitrary locality and availability in binary codes"
    - "locally recoverable codes on algebraic curves"
    - "repair locality with multiple erasure tolerance"
    - "locality and availability in distributed storage"
    - "optimal locally repairable and secure codes for distributed storage systems"
    - "codes with locality for two erasures"

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"
    - "target set selection problem for honeycomb networks"

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"
    - "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.DM(Discrete Mathematics)